Thesis Submitted to Kurukshetra University For the …ckverma.com/Downloads/CKThesis.pdf1 Efficiency and Security Optimization for Fingerprint Biometric System Thesis Submitted to

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Efficiency and Security Optimization for

Fingerprint Biometric System Thesis Submitted to Kurukshetra University

For the Award of Degree

Of

Doctor of Philosophy In Computer Science and Applications

Submitted By:

Chander Kant

Under the supervision of

Dr. Rajender Nath Reader, Department of Computer Science & Applications

Kurukshetra University, Kurukshetra

Department of Computer Science & Applications Kurukshetra University, Kurukshetra

2009

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CERTIFICATE

This is to certify that the thesis entitled “Efficiency and Security optimization for

Fingerprint Biometric System” submitted by Mr. Chander Kant for the award of

degree of Doctor of Philosophy in Computer Science and Applications to

Kurukshetra University, Kurukshetra is a record of bonafide research work carried

out by him under my supervision and guidance. The thesis in my opinion, worthy for

consideration for the award of Doctor of Philosophy in accordance with the

regulations of Kurukshetra University, Kurukshetra. The results embodied in this

thesis have not been submitted to any other institute or university for the award of

any degree or diploma.

(Rajender Nath) Reader, Department of Computer Science & Applications Kurukshetra University, Kurukshetra

DEPARTMENT OF COMPUTER SCIENCE & APPLICATIONS KURUKSHETRA UNIVERSITY KURUKSHETRA

HARYANA (INDIA)

No: ____________

Dated: __________

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No one can do everything all without someone’s help and guidance. Here I would like to mention the

name of those concerned persons, without their guidance and support I might not have been able to

complete this research work.

First of all I would like to thank my supervisor Dr. Rajender Nath, Reader, Department of Computer

Science & Applications, Kurukshetra University, Kurukshetra, for providing me the opportunity to

work under his guidance in an exciting and challenging field of research. His constant motivation,

support and infectious enthusiasm has guided me throughout my research work. I have been

privileged to work under his supervision, and I truly appreciate his help. His encouraging words

have often pushed me to put in my best possible efforts.

I express my sincere thanks to Prof. R.K. Chauhan, Chairman, Deptt. of Computer Science &

Application, Kurukshetra University, Kurukshetra for providing me computer and library facilities

in the Department. I also thank to all my colleagues in the Department for helping me directly and

indirectly.

My Special thanks goes to Sh. Layak Ram Dabbas, Inspector General, Madhuban, Karnal for

helping me acclimatize to the Forensic Science Laboratory during the research. I also thank

Inspector Sh. Ranbir Singh, Sh. Mahavir Singh and Sub Inspector Sh. Ramvir Singh, Sh. Bharat

Singh for their assistance in conducting the experimental work in the biometrics research

laboratory.

I am also thankful to Dr. Surya Kant and Dr. (Mrs.) Pragya Kant, Scientists at University of Guelph,

Canada for their help and support in the field of biometrics.

Finally, I would like to thank my parents and in-laws for their blessings and cooperation. I also

thank to my wife Sheetal Verma for her valuable suggestions during research work and my sweet

daughter Vaanya for providing cheerful environment throughout my research work.

Chander Kant

Acknowledgement

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Table of Contents List of publications 1

List of Figures 10

List of Tables 12

Abstract 13

Chapter1. Introduction 15

1.1 Methods of Identification and Verification 17

1.2 Biometric Techniques 18

1.2.1 Physical Characteristics Based Techniques 18

1.2.2 Behavioral Characteristics Based Techniques 21

1.3 Fingerprints as Biometric Trait 24

1.3.1 Fingerprint Patterns 24

1.3.2 Minutia Points 24

1.3.3 Fingerprint Matching 25

1.4 Fingerprint Classification 26

1.4.1 Fingerprint Feature Extraction 29

1.4.2 Accuracy and Integrity of System 30

1.4.3 Types of Biometric Scanners 30

1.5 Multibiometrics 33

1.5.1 Sources of Biometric Information 33

1.5.2 Levels of Fusion 36

1.6 Soft Biometric 38

Chapter 2. Literature Survey 42

2.1 Comparison of various Biometric Technologies 61

Chander Kant

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Chapter 3. Improving Security in Biometric Systems 67

3.1 Introduction 67

3.1.1 Threat Categorization in Biometric Security Systems 72

3.2 Securing Data using Biometric Cryptography 74

3.2.1 Problems to generate Biometric Cryptographic Key 75

3.2.2 Methods to Secure Biometric Key 76

3.2.3 Existing System: Traditional Cryptography 77

3.2.4 Proposed System: Biometric Cryptography 78

3.2.5 Enrollment / Verification Process 78

3.2.6 Biometric Encryption using other Biometric Templates 80

3.2.7 Advantages of Biometric Encryption 81

3.3 Securing Biometric Data Using Steganography 82

3.3.1 Steganographic Methods 83

3.3.2 LSB Insertion Method of Steganography 85

3.3.3 Limitations of LSB Insertion 87

3.3.4 Steganography in Biometrics 88

3.3.5 Applying Steganography in Biometrics 89

3.3.6 What happens to Pixel Value during Insertion? 91

3.4 Securing Biometric Data Using Cancelable Biometrics 93

3.4.1 Cancelable Biometrics 95

3.4.2 Proposed Work: Protection of Biometric Template 97

3.5 Summary 101

Chapter 4. Making Biometric Systems More Efficient 103

4.1 Introduction 103

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4.2 Reducing Process-Time for Fingerprint Identification System 105

4.2.1 Henry Classification Scheme 107

4.2.2 The Proposed Method to Speed-up Fingerprint Matching Process 110

4.3 Performance Elevation of Fingerprint Verification System 116

4.3.1 Critical Factors Identified Affecting the Performance of 117 Biometric System

4.3.2 Memory Requirements for Biometric Templates 119

4.3.3 The Proposed Approach to Improve Response Time of 119 Biometric System

4.3.4 Performance Estimation for the Proposed Approach 122

4.4 Performance Improvement by using Soft Biometric Traits 123

4.4.1 The Proposed Scheme to Integrate Soft Biometrics with 127 Primary Biometrics 4.4.2 Performance Estimation of Proposed Scheme 128

4.5 Summary 130

Chapter 5. Conclusion and Future Scope 132

Bibliography 137

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List of Publications

Publications in International Journals

1) “Improving Biometric security using Cryptography” published in

International Journal of Advance Research in Computer Engineering Vol-I Jan-

Dec 2007 PP 33-38. ISSN 0974-4320.

2) "Elevating Fingerprint Verification System", published in International

Journal of Physical Sciences. Vol. 19 (I) April 2007. PP 35-38 ISSN 0970-9150.

3) “Fingerprint As Biometric Traits: An Overview” Published in International

journal of Computer Science and knowledge Engineering Vol-I Jan-Dec 2007 PP

33-38. ISSN 0973-6735.

4) “Biometrics Security using Steganography” published in CSC online Journal

“International Journal of Security” Malashiya Vol-II Issue-I, PP 1-5 2008.

www.cscjournals.com. ISSN 1985-2320.

5) “Secure online Business: Exploring the security threats to e-commerce”

published in International journal of Intelligent Information Processing Vol-I

Jan-Dec 2007 PP 1-8. ISSN 0973-3892.

6) “Reducing Process-Time for Fingerprint Identification System” published in

“International Journals of Biometric and Bioinformatics” Malaysia Vol-III Issue–

I, 2009 PP 1-9. www.cscjournals.com ISSN 1985-2347.

7) "Soft Biometric: An Asset for Personal Recognition" published in

International Journal of Computing Science & Communication Technologies

[IJCSCT]. Vol-I, Issue-II, 2009 PP 160-163. ISSN - 0974-3375.

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Publications in Book

1. "Biometric Sensor Based on Optical Frustrated Total Internal Reflection"

in the Book titled “Bioinformatics Computing” published by Narosa Publication,

New Delhi 2007 PP 118-123, ISBN: 978-817319-794-9.

Publications in International-Level Conferences

1. "Protection of Database Template using Cancelable Biometric" published in

IEEE-International advance computing conference 6-7 March 2009, Thapar

University, Patiala.

2. "Improving Fingerprint Verification System” Published in International

Conference on Intelligent Systems & Networks, IISN-2007, Feb. 23-25 2007. PP

298-300.

3. "Soft Biometric: An Asset for Personal Recognition" published in

International Conference on Advanced Computing & Communication

Technologies at APIIT India, Panipat, November 08-09, 2008.

4. "Challenges in Biometrics" published in International Conference on Emerging

trends in Computer Sc. & IT April 23, 2008 at AL-FALAH School of

Engineering & Tech, Faridabad PP 69-77.

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Publications in National-Level Conference/Seminar

1. "Off-line Optical Frustrated Total Internal Reflection" Published in the

proceedings of National Seminar on “Information and Communication

Technology- Recent Advances & Applications ICT-2006 PP 237-240 Feb 09-11,

2006 at JMIT, Radaur Yamuna Nagar.

2. “Web security using Biometrics” published in National Conference on Total

Quality Management held at Vaish College of Engg, Rohtak 10th March 2007.

3. “Biometric Recognition System: An Introduction” published in National level

seminar on Convergence of IT and Management on 24-Nov.2007 at TIMT,

Yamunanagar.

4. “Role of Biometrics in Modern Business” published in National seminar on

"Emerging Challenges in Commerce and Management", Govt. College for

Women, KARNAL 18-19 March, 2008.

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List of Figures

Figure1.1 Enrollment Process in Biometrics System

Figure1.2 Verification and Identification Process

Figure 1.3 Basic patterns of fingerprint

Figure 1.4 Minutiae points in fingerprint

Figure 1.5 Matching minutiae points in two fingerprints

Figure 1.6 Fingerprint categories

Figure 1.7 Flowchart for fingerprint classification algorithm.

Figure 1.8 Steps for Extraction of minutiae points from input fingerprint

Figure 1.9 (a) Optical Sensor and (b) Solid State Sensor.

Figure 1.10 Various source of biometric information

Figure 1.11 Fusion at various levels in a biometric system.

Figure 1.12 Commonly used Soft biometric traits

Figure3.1. Eight different attack points in a biometric authentication system.

Figure3.2. Traditional cryptography system

Figure 3.3 Enrollment process for Biometric Encryption

Figure 3.4 Decryption process for Biometric at verification end

Figure 3.5 Use of Steganography for creating secret Templates

Figure 3.6 Image morphing using Cancelable Biometric

Figure 3.7 Overview of Attack System.

Figure3.8 Blocks in the original image are subsequently scrambled randomly.

Figure 4.1 Block diagram of Fingerprint Identification system.

Figure 4.2 Palmprints of a single person.

Figure 4.3 Proposed Classifier for Fingerprint Identification.

Figures 4.4 Position of Deltas in different Fingerprints.

Figure 4.5 Minutiae- points on a fingerprint.

Figure 4.6 Working of proposed Fingerprint classifier

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Figure 4.7 Steps for Extraction of minutiae points from input fingerprint

Figure 4.8 Measuring Thumb Dimensions during Phase-I

Figure 4.9 Architecture of Proposed Approach

Figure 4.10 Existing Soft Biometric System for person recognition.

Figure 4.11 Architecture of Personal Identification using Primary Biometrics and

Soft Biometric.

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List of Tables Table 1.1 Identification Technologies

Table 2.1 Comparison of various biometric traits.

Table 3.1 Working of the first algorithm for insertion of bit 0 in image pixel value.

Table 3.2 Working of the first algorithm for insertion of bit 1 in image pixel value.

Table 4.1 Henry classification table

Table 4.2 Data Size for Biometric Templates

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Abstract Efficiency and Security optimization for Fingerprint Biometric System

User authentication systems that are based on knowledge such as password or

physical tokens such as ID card are not able to meet strict security performance

requirements of a number of modern computer applications. These applications

generally use computer networks (e.g., Internet), affect a large portion of population,

and control financially valuable tasks (e.g., e-commerce). Biometrics-based

authentication systems are good alternatives to the traditional methods. These

systems are more reliable as biometric data can not be lost, forgotten, or guessed and

more user-friendly because we don’t need to remember or carry anything. In spite of

these advantages of biometric systems over traditional systems, there are many

unresolved issues, for example, how secure are biometric systems against attacks?

How can we guarantee the integrity of biometric templates? How can we combine

cryptography with biometrics to increase overall system security? Can we improve

the feature of existing biometric system, in respect of accuracy and speed?

In this dissertation, we address these issues and develop algorithms and techniques to

eliminate the above said problems and also improve the overall functioning of

biometric devices. First, we analyze the weak points of biometrics and develop

algorithms to make it strong by applying cryptography, steganography and

cancelable biometrics. The proposed approaches are shown to be very successful in

improving the security of biometric devices. Further, we have also developed various

schemes to enhance the accuracy and speed of a biometric system.

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Thesis Contributions

In this dissertation, the problems namely, (i) Biometric systems against imposter

attacks, (ii) Protection of biometric templates, (iii) Enhancement the capabilities of

biometric devices (iv) Combining cryptography and steganography with biometrics,

has been analyzed and provided appropriate solutions for them. The dissertation

includes:

1. Design and development of biometric system to enhance the capability of

fingerprint verification system.

2. Design and development of biometric system to protect the database templates

against imposter attack using cancelable biometrics.

3. Design and development of biometric system using steganography to increase the

security of image based biometric templates.

4. Design and development of a practical biometric cryptosystem utilizing fingerprint

features that combine the biometric systems with cryptographic systems.

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Chapter 1

Introduction

Biometric recognition refers to the use of distinctive physiological (fingerprint, face,

retina, hand geometry, iris etc.) and behavioral (voice, gait, signature etc.)

characteristics, called biometric identifiers or simply biometrics. A reliable

identification system is a critical component in several applications that contribute

their services specifically to genuine users. Examples of such applications include

physical access control to a secure facility, e-commerce, access to computer

networks, attendance mark etc. Traditional methods of establishing a person’s

identity include knowledge-based (e.g., passwords) and token-based (e.g., ID cards)

mechanisms. These representations of the identity can easily be lost, shared or stolen

as stated by Jain A.K [JAI, 1999]. Therefore, they are not sufficient for identity

verification in the modern day world.

A biometric system is essentially a pattern recognition system that operates by

acquiring biometric data from an individual, extracting a feature set from the

acquired data, and comparing this feature set against the template set in the database.

Depending on the application context, a biometric system may operate either in

verification mode or identification mode. Before the system can be put into

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verification or identification mode, a system database consisting of biometric

templates must be created through to process of enrollment.

Enrollment is the process where a user’s initial biometric sample(s) are collected,

assessed, processed, and stored for ongoing use in a biometric system as shown in

Figure 1.1. If users are experiencing problems with a biometric system then they

have to re-enroll to gather higher quality data.

Figure1.1 Enrollment Process in Biometrics System

Biometric system provides two main functionalities viz. verification and

identification. Figure 1.2 shows the flow of information in verification and

identification systems. In verification, also called authentication, the user claims an

identity and the system verifies whether the claim is genuine. If the user’s input and

the template of the claimed identity have a high degree of similarity, then the claim

is accepted as “genuine”. Otherwise, the claim is rejected and the user is considered

as “fraud”. In Identification, the user’s input is compared with the templates of all

the persons enrolled in the database and the identity of the person whose template

has the highest degree of similarity with the user’s input is output by the biometric

system. Typically, if the highest similarity between the input and all the templates is

less than a fixed minimum threshold, the system rejects the input, which implies that

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the user presenting the input is not one among the enrolled users. Therefore, the

matching is 1:N in an identification system.

Figure1.2 Verification and Identification Process

1.1 Methods of identification and verification

A user can be identified or verified on the basis of – (i) Something he knows: e.g. a

password, a PIN etc. (ii) Something he holds: e.g. a credit card, a key, or a passport

(iii) Something he is (biometrics): e.g. a fingerprint, face, iris etc as shown in

Table1.1.

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Using something we know and hold are two easy identification/verification solutions

widely used today. Using something we know only requires a good memory, but

sometime can easily be guessed. An item we hold can be stolen and later on can be

used or copied. Biometrics is the only thing which doesn’t need to be remembered or

carried out.

Table 1.1 Identification Technologies Methods Examples Problems What we Know Password, PIN,

ID Forgotten, Shared, easy to guess

What we have Key, Cards, etc Lost or Stolen, Can be duplicated

What we are Fingerprint, Face, Iris…

Non-repudiable authentication

Since fingerprints, iris patterns, etc. all are parts of our body and thus not easily

misplaced, stolen, forged, or shared. One way to increase security in an

identification/verification system is to combine two or more different biometric

traits.

1.2 Biometric techniques There are many different techniques available to identify/verify a person based on

biometrics as suggested by U.K. Biometric Working Group [UKBWG, 2003]. These

techniques can be divided into physical characteristics and behavioral characteristics

based techniques.

1.2.1 Physical characteristics Based Techniques Biometrics techniques based on physical characteristics of human being such as

finger print, hand geometry; palm print etc are called physical characteristics based

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techniques. Following are examples of biometric techniques based on physical

characteristics [MAL, 2003].

Fingerprint recognition: Finger printing takes an image (either using ink or a digital

scan) of a person's fingertips and records its characteristics. Whorls, arches, and

loops are recorded along with the patterns of ridges, furrows, and minutiae. This

information may then be processed or stored as an image or as an encoded computer

algorithm to be compared with other fingerprint records.

In this technique, the user presses his finger gently against a small reader surface

(optical or silicon) usually of about 2 inch square size. This reader is attached to a

computer and takes the information from the scan and sends it to the database. There

it is compared to the information within. The user is usually required to leave his

finger on the reader for less than 5 seconds during which time the identification or

verification takes place

Hand geometry: Hand goemetry involves the measurement and analysis of the shape

of one's hand. It is a fairly simple procedure and is surprisingly accurate. Though it

requires special hardware to use, it can be easily integrated into other devices or

systems. Unlike fingerprints, the human hand isn't unique. Individual hand features

are not descriptive enough for identification. However, it is possible to devise a

method by combining various individual features and measurements of fingers and

hands for verification purposes.

In this technique, the user places the palm of his hand on a metal surface which has

guidance pegs on it. The hand is properly aligned by the pegs so the device can read

the hand attributes. The device then checks its database for verification of the user.

The process usually takes less than 5 seconds. Current hand geometry scanners do

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not have any way to detect whether a hand is living or not and therefore can be

fooled by a fake hand if pressure is applied to the plate correctly.

Face recognition: Facial recognition analyzes the characteristics of a person's face

images input through a digital video camera. It measures the overall facial structure,

including distances between eyes, nose, mouth, and jaw edges. These measurements

are retained in a database and used as a comparison when a user stands before the

camera. This biometric has been widely, and perhaps wildly, touted as a fantastic

system for recognizing potential threats (whether terrorist, scam artist, or known

criminal) but so far has been unproven in high-level usage. It is currently used in

verification only systems with a good deal of success.

In this technique, the user faces the camera, standing about two feet from it. The

system will locate the user's face and perform matches against the claimed identity or

the facial database. It is possible that the user may need to move and reattempt the

verification based on his facial position. The system usually comes to a decision in

less than 5 seconds.

Vein pattern recognition: Vascular patterns are best described as a picture of the

veins in a person's hand or face. The thickness and location of these veins are

believed to be unique enough to an individual to be used to verify a person's identity.

The most common form of vascular pattern readers are hand-based, requiring the

user to place their hand on a curved reader that takes an infrared scan. This scan

creates a picture that can then be compared to a database to verify the user's stated

identity

Retina recognition: Retinal scanning analyses the layer of blood vessels at the back

of the eye. Scanning involves using a low-intensity light source and an optical

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coupler and can read the patterns at a great level of accuracy. It does require the user

to remove glasses, place their eye close to the device, and focus on a certain point.

Whether the accuracy can outweigh the public discomfort is yet to be seen.

In this technique, the user looks through a small opening in the device at a small

green light. The user must keep their head still and eye focused on the light for

several seconds during that time period the device will verify his identity. This

process takes about 10 to 15 seconds in total. There is no known way to replicate a

retina, and a retina from a dead person would deteriorate too fast to be useful, so no

extra precautions have been taken with retinal scans to be sure the user is a living

human being.

Iris recognition: Iris scans analyze the features that exist in the colored tissue

surrounding the pupil which has more than 200 points that can be used for

comparison, including rings, furrows and freckles. The scans use a regular video

camera style and can be done from further away than a retinal scan. It will work

through glasses fine and in fact has the ability to create an accurate enough

measurement that it can be used for identification purposes, and not just for

verification.

In this technique, the user places himself so that he can see his own eye's reflection

in the device. The user may be able to do this from up to 2 feet away or may need to

be as close as a couple of inches depending on the device. Verification time is

generally less than 5 seconds, though the user will only need to look into the device

for a couple of moments. To prevent a fake eye from being used to fool the system,

these devices may vary the light shone into the eye and watch for pupil dilation.

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1.2.2 Behavioral Characteristics based Techniques Those Biometrics techniques which are based on the behavior of human being such

as voice, signature, gait, keystroke etc. are called behavioral characteristics based

techniques. Following are examples of biometric techniques based on behavioral

characteristics.

Voice recognition: Voice recognition systems use characteristics of the voice, such

as pitch, tone, and frequency. Focuses on differences resulting from the shape of

vocal tracts and learned speaking habits. This operates best when there’s no

background noise.

Signature recognition: The text involved in a signature, in general is continuous and

regular in nature. The user signs on a tablet or on paper that laying over a sensor

tablet. The device records the signature and compares it to its database. In this

technique, the verification takes about 5 seconds.

Keystrokes dynamics: The user types his password or phrase on the keyboard. The

system then records the timing of the typing and compares the password itself and

the timing to its database. Here, verification takes less than 5 seconds.

In ‘Handbook of Fingerprint Recognition’ Maltoni D, Maio. D., Jain A.K, and

Prabhakar.S [MAL, 2003] suggests that biometric identifiers can also be

combination of distinctive physiological and behavioral characteristics. We can

compare different physiological and behavioral biometrics on the basis of various

parameters and apply them to any security system on these parameters.

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1.3 Fingerprint as Biometrics traits Among all the biometric techniques, fingerprint-based identification is the oldest

method, which has been successfully used in numerous applications. Everyone is

known to have unique, immutable fingerprints. The uniqueness of a fingerprint can

be determined by the pattern of ridges and furrows as well as the minutiae points. A

smoothly flowing pattern formed by ridges and furrows on the hand is called a palm

print. A fingerprint is believed to be unique to each person. Fingerprints of even

identical twins are different. Fingerprints are one of the most mature biometric

technologies and are also considered legitimate proofs of evidence in courts of law

all over the world. Fingerprints are, therefore, used in forensic divisions worldwide

for criminal investigations. More recently, an increasing number of civilian and

commercial applications are either using or actively considering using fingerprint-

based identification because of a better understanding of fingerprints as well as

demonstrated matching performance than any other existing biometric technology

[HIL, 2001]. The analysis of fingerprints for matching purposes generally requires

the comparison of several features of the print pattern. These include patterns, which

are aggregate characteristics of ridges, and minutia points, which are unique features

found within the patterns [JAI, 1999A]. It is also necessary to know the structure and

properties of human skin in order to successfully employ some of the imaging

technologies.

1.3.1 Fingerprint Patterns

The three basic patterns of fingerprint ridges are the arch, loop, and whorl as shown

in Figure 1.3. An arch is a pattern where the ridges enter from one side of the finger,

rise in the center forming an arc, and then exit the other side of the finger (see Figure

1.3 a). The loop is a pattern where the ridges enter from one side of a finger, form a

curve, and tend to exit from the same side they enter (see Figure 1.3 b).

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(a) Arch (b) Loop (c) Whorl

Figure 1.3 Basic Patterns of Fingerprint

In the whorl pattern, ridges form circularly around a central point on the finger (see

Figure 1.3 c). Scientists have found that family members often share the same

general fingerprint patterns, leading to the belief that these patterns are inherited

[JON, 2005].

1.3.2 Minutia Points The major Minutia points in fingerprint are: ridge ending, bifurcation, and short ridge or dot as shown in Figure 1.4.

(a) Ridges Ending (b) Ridges Bifurcation (c) Dot Figure 1.4 Minutiae points in fingerprint

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The ridge ending is the point at which a ridge terminates (see Figure 1.4 a).

Bifurcations are points at which a single ridge splits into two ridges (see Figure 1.4

b). Short ridges or dots are ridges which are significantly shorter than the average

ridge length on the fingerprint (see Figure 1.4 c). Minutiae and patterns are very

important in the analysis of fingerprints since no two fingers have been shown to be

identical.

1.3.3 Fingerprint Matching

A fingerprint is made up of a series of ridges and furrows on the surface of the

finger. The uniqueness of a fingerprint can be determined by the pattern of ridges

and furrows as well as the minutiae points. Minutiae points are local ridge

characteristics that occur at either a ridge bifurcation or a ridge ending. Fingerprint

matching techniques can be divided into two categories: minutiae-based and

correlation based. Minutiae-based techniques first find minutiae points and then map

their relative placement on the finger as shown in Figure 1.5

Figure 1.5 Matching minutiae points in two fingerprints

However, there are some difficulties when using this approach. It is difficult to

extract the minutiae points accurately when the fingerprint is of low quality. Also

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this method does not take into account the global pattern of ridges and furrows. The

correlation-based method is able to overcome some of the difficulties of the

minutiae-based approach. However, it has some of its own shortcomings.

Correlation-based techniques require the precise location of a registration point and

are affected by image translation and rotation. Fingerprint matching based on

minutiae has problems in matching different sized (unregistered) minutiae patterns.

Local ridge structures can not be completely characterized by minutiae. A

commercial fingerprint-based authentication system requires a very low False Reject

Rate (FAR) for a given False Accept Rate (FAR).

1.4 Fingerprint Classification

To reduce the search time and computational complexity, it is desirable to classify

the fingerprints in an accurate and consistent manner so that the input fingerprint is

required to be matched only with a subset of the fingerprints in the database. Figure

1.6 shows the different classifications of fingerprint pattern.

Figure 1.6 Fingerprint categories: (a) arch, (b) tented arch, (c) left loop, (d) right

loop, (e) whorl, and (f) twin loop.

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Fingerprint classification is a technique to assign a fingerprint into one of the several

pre-specified types, which can provide an indexing mechanism. Fingerprint

classification can be viewed as a coarse level matching of the fingerprints.

Figure 1.7 Flowchart for fingerprint classification algorithm.

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An input fingerprint is first matched at a coarse level to one of the pre-specified

types and then, at a finer level, it is compared to the subset of the database containing

that type of fingerprints only. An algorithm (as shown in Figure 1.7 above) has been

developed to extract the classification of fingerprint i.e. whether it is whorl, right

loop, left loop, arch, or tented arch. The classification algorithm summarized here for

determining the class of a fingerprint and conducts simpler tests in the decision tree.

The algorithm works on the basis of calculating singular points, symmetry and ridge

structures as explained below:

1. Singular Points: Number of delta (ND) and core (NC) are main singular points in a

fingerprint to determine the fingerprint classification. The algorithm shown in Figure

1.7 is based on singular points and ridges orientation.

2. Symmetry: The feature extraction stage also estimates an axis locally symmetric to

the ridge structures at the core and computes � the number of ridges crossing the line

segment joining core and delta. The relative position, R, of delta with respect to

symmetry axis is determined as follows R = 1 if the delta is on the right side of

symmetry axis otherwise R = 0.

3. Ridge Structure: The classifier not only uses the orientation information but also

utilizes the structural information in the extracted ridges. This feature summarizes

the overall nature of the ridge flow in the fingerprint.

The algorithm summarized here for determining the class of a fingerprint. Two deltas

points are typically detected for a whorl, which is an easier condition to detect.

Further if it does not detect two deltas, it proceeds on the basis of single delta and

find Left-Loop, Right-Loop or Tented Arch with the help of � and R as shown in

Figure 1.7. In last if no deltas is found then algorithm marks the fingerprint as Arch.

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1.4.1 Fingerprint Feature Extraction

The main reason behind the use of fingerprint biometric is that it is the most proven

technique to authenticate the individual. The fingerprint is basically the combination

of ridges and valleys on the surface of the finger. The lines that create fingerprint

pattern are called ridges and the spaces between the ridges are called valleys or

furrows. The major steps involved in fingerprint recognition using minutiae

matching approach after image acquisition are image enhancement, minutiae

extraction as shown in figure 1.8.

Figure 1.8 Steps for Extraction of minutiae points from input fingerprint

Once a high-quality image is captured, there are a several steps required to convert

its distinctive features into a compact template. This process is known as feature

extraction. If the image is grayscale, areas lighter than a particular threshold are

discarded, and those darker are made black. The goal of fingerprint enhancement is

to increase the clarity of ridge structure so that minutiae points can be easily and

correctly extracted [multimodal system for efficient human recognition]. The

enhanced fingerprint image is binarized and submitted to the thinning algorithm

which reduces the ridge thickness to one pixel wide for precise location of endings

and bifurcations. Minutiae localization begins with this processed image. The

30

processed image is used to extract minutiae points which are the points of ridge

endings and bifurcations. The location of minutiae points along with the orientation

is extracted and stored to form feature set. A minutia based matching consists of

finding alignment between the template and the input minutiae sets that result in the

maximum number of minutiae pairings.

1.4.2 Accuracy and Integrity of System

With any security system users always wonder that, “Can this security system be

beaten”? In most cases, false negatives (a failure to recognize a legitimate user) are

more likely than false positives. Overcoming a fingerprint system by presenting it

with a "false or fake" fingerprint is likely to be a difficult deed. However, such

scenarios can be tried, and the sensors on the market use a variety of means to

circumvent them. For instance, someone may attempt to use latent print residue on

the sensor just after a legitimate user accesses the system. At the other end of the

scale, there is the gruesome possibility of presenting a finger to the system that is no

longer connected to its owner. Therefore, sensors attempt to determine whether a

finger is live, and not made of latex (or worse). Detectors for temperature, blood-

oxygen level, pulse; blood flow, humidity, or skin conductivity would be integrated.

1.4.3 Types of Biometric Scanners

Acquiring high-quality images of distinctive fingerprint ridges and minutiae is a

complicated task. The fingerprint is a small area from which to take measurements,

and the wear of daily life affects which ridge patterns show most prominently.

Increasingly sophisticated mechanisms have been developed to capture the

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fingerprint image with sufficient detail and resolution. The technologies in use today

are optical, silicon, and ultrasound.

Optical technology is the oldest and most widely used. The finger is placed on a

coated platen, usually built of hard plastic but proprietary to each company. In most

devices, a charged coupled device (CCD) converts the image of the fingerprint, with

dark ridges and light valleys, into a digital signal. The brightness is either adjusted

automatically (preferable) or manually (difficult), leading to a usable image.

Optical devices have several strengths over the other types of scanners: i) they are

the most proven over time, ii) they can withstand, to some degree, temperature

fluctuations; iii) they are fairly inexpensive; and iv) they can provide resolutions up

to 500 dpi. Drawbacks to the technology include size - the platen must be of

sufficient size to achieve a quality image - and latent prints. Latent prints are leftover

prints from previous users. This can cause image degradation, as severe latent prints

can cause two sets of prints to be superimposed. Also, the coating and CCD arrays

can wear with age, reducing accuracy.

Optical is the most widely used technology. Thus most prominent fingerprint

companies utilize optical technology. Though majority of companies use optical

technology yet increasing number of vendors also use silicon technology.

Silicon technology has gained considerable acceptance since its introduction in the

late 90's. Most silicon, or chip, technology is based on DC capacitance. The silicon

sensor acts as one plate of a capacitor, and the finger is the other. The capacitance

between platen and the finger is converted into an 8-bit grayscale digital image. With

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the exception of AuthenTec, whose technology employs AC capacitance and reads to

the live layer of skin, all silicon fingerprint vendors use a variation of this type of

capacitance.

Silicon generally produces better image quality, with less surface area, than optical.

Since the chip is comprised of discreet rows and columns - between 200-300 lines in

each direction on a 1cmx1.5cm wafer - it can return exceptionally detailed data. The

reduced size of the chip means that costs should drop significantly, now that much of

the R&D necessary to develop the technology is bearing fruit. Silicon chips are small

enough to be integrated into many devices which cannot accommodate optical

technology.

Figure 1.9 (a) Optical Sensor (b) Solid State Sensor.

Silicon's durability, especially in sub-optimal conditions, has yet to be proven.

Although manufacturers use coating devices to treat the silicon, and claim that the

surface is 100x more durable than optical, this has to be proven. Also, with the

reduction in sensor size, it is even more important to ensure that enrolment and

verification are done carefully - a poor enrollment may not capture the center of the

33

fingerprint, and subsequent verifications are subject to the same type of placement.

Many major companies have recently moved into the silicon field. Infineon (the

semiconductor division of Siemens) and Sony have developed chips to compete with

Veridicom (a spin-off of Lucent), the leader in silicon technology.

Ultrasound technology, though considered perhaps the most accurate of the

fingerprint technologies, is not yet widely used. It transmits acoustic waves and

measures the distance based on the impedance of the finger, the platen, and air.

Ultrasound is capable of penetrating dirt and residue on the platen and the finger,

countering a main drawback to optical technology.

Until ultrasound technology gains more widespread usage, it will be difficult to

assess its long-term performance. However, preliminary usage of products from

Ultra-Scan Corporation (USC) indicates that this is a technology with significant

promise. It combines a strength of optical technology, large platen size and ease of

use, with a strength of silicon technology, the ability to overcome sub-optimal

reading conditions.

1.5 Multibiometric

A Multibiometric system uses multiple sensors for data acquisition. It captures

multiple samples of a single biometric trait (called multi-sample biometrics) or

samples of multiple biometric traits (called multi-source or multimodal biometrics).

Multibiometric systems promise significant improvement over single biometric

systems, for example, higher accuracy and increased resistance to spoofing. They

also claim to be more universal by enabling a user who does not possess a particular

34

biometric identifier to still enroll and authenticate using other traits, thus eliminating

enrollment problems.

1.5.1 Sources of Biometric Information A Multibiometric system relies on the evidence presented by multiple sources of

biometric information. Based on the nature of these sources, a Multibiometric system

can be classified into one of the following five categories as shown in Figure 1.10.

(1) Multiple Instances: In these systems, a single sensor is used to acquire multiple

instances of same biometric trait in order to account for the variations that can occur

in the trait. For example, a face system may capture (and store) the frontal profile of

a person’s face along with the left and right profiles in order to account for variations

in the facial pose as explained by Uludag U. [ULU, 2004]. It is an inexpensive way

of improving system performance since this system requires neither multiple sensors

nor multiple feature extraction and matching modules.

(2) Multiple Sensors: These biometric systems capture information from different

sensors for the same biometric trait. For example, optical, solid-state, and ultrasound

based sensors are available to capture fingerprints; an infrared sensor may be used in

conjunction with a visible-light sensor to acquire the face image of a person [JAI

2006] , a multi-spectral camera may be used to acquire images of the iris, face or

finger [ULU, 2005].

(3) Multiple Representations: In these systems, the same biometric data is processed

using multiple algorithms in order to improve matching performance. They can use

either multiple feature sets (i.e. multiple representations) extracted from the same

biometric data or multiple matching schemes operating on a single feature set.

35

Figure 1.10 Various Source of Biometric Information

(4) Multiple units: These systems use multiple units of the same biometric trait. For

example, the left and right index fingers, or the left and right irises of an individual

may be used to verify authenticity of a person Ratha N [RAT, 2001]. These systems

are beneficial for those users whose biometric traits cannot be reliably captured due

to inherent problems. These systems also ensure the presence of a live user by asking

the user to provide a random subset of biometric measurements (e.g., left middle

finger followed by right index finger).

36

(5) Multiple biometric traits: In these systems, multiple biometric traits of an

individual are used to establish the identity. Such systems employ multiple sensors to

acquire data pertaining to different traits. Voderhobli K [VOD 2006] used the face

and voice traits of an individual for identification. The cost of deploying these

systems is substantially more due to the requirement of new sensors and,

consequently, the development of appropriate user interfaces. The number of traits

used in a specific application is governed by practical considerations such as the cost

of deployment, enrollment time, expected error rate, etc. However, first four

scenarios still suffer from some of the problems faced by unimodal systems. Fifth

scenario, i.e. a multimodal biometric system based on multiple traits is expected to

be more robust to noise, address the problem of non-universality, improve the

matching accuracy, and provide reasonable protection against spoof attacks.

1.5.2 Levels of Fusion A generic biometric system has four important modules: (a) the sensor module which

captures the trait in the form of raw biometric data; (b) the feature extraction module

which processes the data to extract a feature set that is a compact representation of

the trait; (c) the matching module which employs a classifier to compare the

extracted feature set with the templates residing in the database to generate matching

scores; (d) the decision module which uses the matching scores to either determine

an identity or validate a claimed identity. In multimodal biometric system

information reconciliation can occur in any of these modules (see Figure1.11). (a)

Fusion at the sensor level: It refers to the consolidation of raw data obtained using

multiple sensors or multiple snapshots of a biometric using a single sensor. (b)

Fusion at feature extraction level: Either the data itself or the feature sets originating

from multiple sensors/sources are fused. (c) Fusion at the match score level: The

scores generated by multiple classifiers pertaining to different modalities are

combined. (d) Fusion at the decision level: the final outputs of multiple classifiers

37

are consolidated via techniques such as majority voting. Biometric systems that

integrate information at an early stage of processing are believed to be more effective

than those systems which perform integration at a later stage [CHA, 1999].

Figure 1.11: Fusion at various levels in a biometric system. FE: Feature Extraction Level; MM: Matching Level; DM: Decision Level; FM: fusion module

Since the feature set contains richer information about the input biometric data than

the matching score or the output decision of a matcher, fusion at the feature level is

expected to provide better recognition results. However, fusion at this level is

difficult to achieve in practice because (i) the feature sets of the various modalities

may not be compatible (e.g., eigen-coefficients of face and minutiae set of finger),

38

and (ii) most commercial biometric systems do not provide access to the feature sets

(nor the raw data) which they use in their products. Fusion at the decision level is

considered to be rigid due to the availability of limited information. Thus, fusion at

the match score level is usually preferred, as it is relatively easy to access and

combine the scores presented by the different modalities.

1.6 Soft Biometric

Soft biometric traits are those characteristics of human being that provide some

information about the individual, but lack of the distinctiveness and permanence to

sufficiently differentiate any two individuals. We define soft biometric traits as

characteristics that provide some information about the individual, but lack the

distinctiveness and permanence to sufficiently differentiate any two individuals. The

soft biometric traits can either be continuous (e.g., height and weight) or discrete

(e.g., gender, eye color, ethnicity, etc.) as shown in Figure 1.12. First personal

identification system developed by Alphonse Bertillon [BER, 1896] for

identification of criminals was based on three sets of features: (i) body measurements

(anthropometry) like height and length of the arm, (ii) morphological description of

the appearance and shape of the body like eye color and anomalies of the fingers,

and (iii) peculiar marks observed on the body like moles, scars, and tattoos.

Although the Bertillon system was very useful in tracking criminals, it had an

unacceptably high rate of false identification. This was due to two reasons. Firstly,

several individuals can have the same set of values for these measurements.

Secondly, for the same individual, these values can change over time.

39

Figure 1.12 Commonly used Soft biometric traits

In other words, these characteristics do not have the distinctiveness and permanence

to uniquely identify an individual over a period of time and hence we refer them as

soft biometric traits. A system that is completely based on soft biometric traits

cannot provide the required accuracy in the recognition of individuals. In fact, the

Bertillon system had a relatively short life since immediately after it was introduced;

the Henry system for fingerprint classification [IBI, 2003] was adopted by the

Scotland Yard for keeping track of criminals. However, soft biometric traits can be

used to improve the performance of a traditional biometric system (e.g., fingerprint,

hand-geometry) in many ways. [WAY, 1997] proposed the use of soft biometric

traits like gender and age, for filtering a large biometric database. Filtering refers to

40

limiting the number of entries in a database to be searched, based on characteristics

of the interacting user. For example, if the user can somehow be identified as a

middle-aged male, the search can be restricted only to the subjects with this profile

enrolled in the database. This greatly improves the speed or the search efficiency of

the biometric system. Filtering reduces the probability of obtaining a wrong match,

but this is offset by the fact that the errors in filtering also reduce the probability of

obtaining a correct match. Hence, in general, filtering drastically reduces the time

required for identification but can degrade the recognition performance. In addition

to filtering, the soft biometric traits can also be used for tuning the parameters of the

biometric system. Biometric systems recognize users based on their physiological

and behavioral characteristics [JAI, 1999]. Unimodal biometric systems make use of

a single biometric trait for user recognition. It is difficult to achieve very high

recognition rates using unimodal systems due to problems like noisy sensor data and

non-universality and/or lack of distinctiveness of the chosen biometric trait.

Multimodal biometric systems address some of these problems by combining

evidence obtained from multiple sources [HON, 1999]. A multimodal biometric

system that utilizes a number of different biometric identifiers like face, fingerprint,

hand-geometry, and iris can be more robust to noise and minimize the problem of

non-universality and lack of distinctiveness. However, a multimodal system will

require a longer verification time thereby causing inconvenience to the users. A

multimodal biometric system based on different biometric identifiers can be

expected to be more robust to noise, address the problem of non-universality,

improve the matching accuracy, and provide reasonable protection against spoof

attacks. However, such a system will have two limitations. Firstly, the overall cost

involved in building the multimodal system can be prohibitively high due to the need

for multiple high quality sensors and increased storage and computational

requirements. Secondly, the system will require a longer verification time thereby

causing inconvenience to the users. Due to these limitations, the number of

41

identifiers in a multimodal biometric system is usually restricted to two or three. A

possible solution to the problem of designing a reliable and user-friendly biometric

system is to use additional information about the user like height, weight, age,

gender, ethnicity, and eye color to improve the performance of the primary biometric

system [KAN, 2008].

The rest of the thesis is divided into the four Chapters.

Chapter 2 discusses the literature survey and analyses the existing work in the field

of biometric in general and fingerprint technology in specific.

Chapter 3 presents three approaches for improving the security of biometric systems.

Chapter 4 discusses three different methods to enhance the performance of biometric

systems by reducing response time.

At last, Chapter 5 presents the conclusion of thesis and gives the scope of future

work.

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Chapter 2

Literature Survey

For the purpose of this thesis, the literature survey covers a period of 1997 to 2008.

The literature work on “Efficiency and Security optimization for Fingerprint

Biometric System” divided into three main areas.

1. Security issues in Biometric System.

2. Performance issues in Biometric System.

3. Comparison of various Biometric Systems.

J. D. Woodward [WOO, 1997] in his paper “Biometrics: Privacy’s Foe or Privacy’s

Friend?” the author state that- Both the public and private sectors are making

extensive use of biometrics for human recognition. As this technology becomes more

economically viable and technically perfected, and thus more commonplace, the

field of biometrics will spark legal and policy concerns. Critics inevitably compare

biometrics to Big Brother and the loss of individual privacy. The biometric lobby

generally stresses the greater security and improved service that the technology

provides. Is biometrics privacy’s friend or privacy’s foe? This paper explores the

various arguments for and against biometrics and contends that while biometrics may

43

pose legitimate privacy concerns, these issues can be adequately addressed. In the

final analysis, biometrics emerges as privacy’s friend.

L. Hong and A. K. Jain [HON, 1998] in their paper “Integrating Faces and

Fingerprints for Personal Identification” -An automatic personal identification

system based solely on fingerprints or faces is often not able to meet the system

performance requirements. Face recognition is fast but not extremely reliable, while

fingerprint verification is reliable but inefficient in database retrieval. We have

developed a prototype biometric system which integrates faces and fingerprints. The

system overcomes the limitations of face recognition systems as well as fingerprint

verification systems. The integrated prototype system operates in the identification

mode with an admissible response time. The identity established by the system is

more reliable than the identity established by a face recognition system. In addition,

the proposed decision fusion scheme enables performance improvement by

integrating multiple cues with different confidence measures. Experimental results

demonstrate that our system performs very well. It meets the response time as well as

the accuracy requirements. Experimental results demonstrate that the system

performs very well. It meets the response time as well as the accuracy requirements.

BelCn Ruiz-Mezcua, D.G. Plaza, C.Fernandez, P.D.Garcia and F. Fernandez [BEL,

1999] in their paper “Biometrics verification in a real environment” describes some

of the biometrics verification applications developed: Multimodal Verification for

Teleservices and Security Applications. These prototypes provide identity

authentication in two of the most promising application areas of biometrics: Secured

access to financial services (ATM and Internet Teleservices) and building security

applications. The verification modalities implemented into the prototypes are face

and speech verification, these are potentially the best accepted by users since they

are the less intrusive and the more inexpensive. To achieve higher levels of security,

44

the results obtained by each modality are combined using a fusion method, which

decides the user acceptance or rejection. Every prototype has been tested by end-

users in a close to real environment.

Yeung and Pankanti [YEU, 1999] describe an invisible fragile watermarking

technique to detect regions in a fingerprint image that have been tampered by an

attacker. In the proposed scheme, a chaotic mixing procedure is employed to

transform a visually perceptible watermark to a random-looking textured image in

order to make it resilient against attacks. This “mixed” image is then embedded in a

fingerprint image. The authors show that the presence of the watermark does not

affect the feature extraction process. The use of a watermark also imparts copyright

capability by identifying the origin of the raw fingerprint image.

Souheil Ben-Yacoub, Yousri Abdeljaoued, and Eddy Mayoraz [BEN, 1999] in

“Fusion of Face and Speech Data for Person Identity Verification” says that

Biometric person identity authentication is gaining more and more attention. The

authentication task performed by an expert is a binary classification problem: reject

or accept identity claim. Combining experts, each based on a different modality

(speech, face, fingerprint, etc.), increases the performance and robustness of identity

authentication systems. In this context, a key issue is the fusion of the different

experts for taking a final decision (i.e., accept or reject identity claim). We propose

to evaluate different binary classification schemes (support vector machine,

multilayer perceptron, C4.5 decision tree, Fisher’s linear discriminant, Bayesian

classifier) to carry on the fusion.

Anil Jain, Arun Ross and Salil Prabhakar [JAI, 2001] in ”Fingerprint matching using

minutiae and Texture Features” state that the advent of solid-state fingerprint

sensors presents a fresh challenge to traditional fingerprint matching algorithms.

45

These sensors provide a small contact area ( apx. 0.6" X 0.6") for the fingertip and,

therefore, sense only a limited portion of the fingerprint. Thus multiple impressions

of the same fingerprint may have only a small region of overlap. Minutiae based

matching algorithms, which consider ridge activity only in the vicinity of minutiae

points, are not likely to perform well on these images due to the insufficient number

of corresponding points in the input and template images. We present a hybrid

matching algorithm that uses both minutiae (point) information and texture (region)

information for matching the fingerprints. Results obtained on the MSU

VERIDICOM database shows that a combination of the texture-based and minutiae-

based matching scores leads to a substantial improvement in the overall matching

performance

Ratha et al. [RAT, 2001A] propose the use of distortion functions to generate

biometric data that can be canceled if necessary. They use a non-invertible

transformation function that distorts the input biometric signal (e.g., face image)

prior to feature extraction or, alternately, modifies the extracted feature set (e.g.,

minutiae points) itself. When a stored template is compromised, then the current

transformation function is replaced with a new function thereby “canceling” the

current (compromised) template and generating a new one. This also permits the use

of the same biometric trait in several different applications by merely adopting an

application-specific transformation function. However, it is not clear how matching

can be accomplished in the transformed domain. In the realm of template

transformation, the so-called biometric cryptosystems are gaining popularity (for a

survey on existing techniques, see [ULU, 2004]. These systems combine biometrics

and cryptography at a level that allows biometric matching to effectively take place

in the cryptographic domain, hence exploiting the associated higher security.

46

Arun Ross, Anil Jain, James Reisman [ARU, 2002] in their paper “A hybrid

fingerprint matcher” the authors states that most fingerprint matching systems rely

on the distribution of minutiae on the fingertip to represent and match "fingerprints.

While the ridge flow pattern is generally used for classifying "fingerprints, it is

seldom used for matching. This paper describes a hybrid "fingerprint matching

scheme that uses both minutiae and ridge flow information to represent and match

"fingerprints. A set of 8-Gabor filters, whose spatial frequencies correspond to the

average inter-ridge spacing in fingerprints, is used to capture the ridge strength at

equally spaced orientations. A square tessellation of the filtered images is then used

to construct an eight-dimensional feature map, called the ridge feature map. The

ridge feature map along with the minutiae set of a fingerprint image is used for

matching purposes. The proposed technique has the following features: (i) the entire

image is taken into account while constructing the ridge feature map; (ii) minutiae

matching is used to determine the translation and rotation parameters relating the

query and the template images for ridge feature map extraction; (iii) filtering and

ridge feature map extraction are implemented in the frequency domain thereby

speeding up the matching process; (iv) filtered query images are catched to greatly

increase the one-to-many matching speed. The hybrid matcher performs better than a

minutiae-based fingerprint matching system. The genuine accept rate of the hybrid

matcher is observed to be apx. 10% higher than that of a minutiae-based system at

low FAR values. Fingerprint verification (one-to-one matching) using the hybrid

matcher on a Pentium III, 800 MHz system takes apx. 1.4 s, while "fingerprint

identification (one-to-many matching) involving 1000 templates takes apx. 0.2 s per

match. Currently, minutiae information is being used to align the query and the

template images, before computing the ridge feature map of the query image. We are

working on non-minutiae based alignment techniques that make use of orientation

field and ridge feature map information to align image pairs. The following areas of

improvement are also being studied: (1) New matching methods for comparing the

47

ridge feature maps of two images. (2) Development of fusion architectures to

improve performance of the hybrid matcher.

Ferri et al [FER, 2002] propose an algorithm to embed dynamic signature features

into face images present on ID cards. These features are transformed into a binary

stream after compression (used in order to decrease the amount of payload data). A

computer-generated hologram converts this stream into the data that is finally

embedded in the blue channel of a face image. During verification, the signature

features hidden in the face image are recovered and compared against the signature

obtained on-line.

O’Gorman [GOR, 2003] in “Comparing Passwords, Tokens, and Biometrics for User

Authentication states” that before the computer age, authenticating a user was a

relatively simple process. One person could authenticate another by visual

recognition, interpersonal communication, or, more formally, mutually agreed upon

authentication methods. With the onset of the computer age, authentication has

become more complicated. Face-to-face visual authentication has largely dissipated,

with computers and networks intervening. Sensitive information is exchanged daily

between humans and computers, and from computer to computer. This complexity

demands more formal protection methods; in short, authentication processes to

manage our routine interactions with such machines and networks. Authentication is

the process of positively verifying identity, be it that of a user, device, or entity in a

computer system. Often authentication is the prerequisite to accessing system

resources. Positive verification is accomplished by means of matching some

indicator of identity, such as a shared secret prearranged at the time a person was

authorized to use the system. The most familiar user authenticator in use today is the

password. The secure sockets layer (SSL) is an example of machine to machine

authentication. Human–machine authentication is known as user authentication and

48

it consists of verifying the identity of a user: is this person really who he claims to

be? User authentication is much less secure than machine authentication and is

known as the Achilles’ heel of secure systems. This paper introduces various human

authenticators and compares them based on security, convenience, and cost. The

discussion is set in the context of a larger analysis of security issues, namely,

measuring a system’s vulnerability to attack. The focus is kept on remote computer

authentication. Authenticators can be categorized into three main types: secrets (what

we know), tokens (what we have), and IDs (who we are). A password is a secret

word, phrase, or personal identification number. Although passwords are

ubiquitously used, they pose vulnerabilities, the biggest being that a short mnemonic

password can be guessed or searched by an ambitious attacker, while a longer,

random password is difficult for a person to remember. A token is a physical device

used to aid authentication. Examples include bank cards and smart cards. A token

can be an active device that yields one-time passcodes (time-synchronous or

challenge–response). This paper defines tokens as portable, secure storage devices

accessed at the client via a password to obtain a passcode (a secret, machine-

generated, and stored number). A biometric ID is a distinguishing feature of the

human body that can be used for authentication. Examples include fingerprints, eyes,

face, hand, voice, and signature. Inherently, a biometric links the authenticator to the

owner, unlike a password, which can be lent or stolen. As such, biometrics are useful

for non-repudiation of a transaction, wherein a user cannot reject or disclaim having

participated in a transaction. Contrary to common assumptions, a biometric can be

counterfeited or copied, albeit with difficulty. Biometrics can be classified further

into subtypes: physical and behavioral. Physical biometrics are based on body

features. Behavioral biometrics are based on learned gestures such as signatures.

This paper proposes a more precise classification nomenclature for biometric signals:

stable and alterable. Stable signals are relatively constant and unchanging. Alterable

signals derive from an underlying stable biometric and a variable.

49

A.K. Jain and U. Uludag [JAI, 2003] in their work “Hiding Biometric Data” states

that with the wide spread utilization of biometric identification systems, establishing

the authenticity of biometric data itself has emerged as an important research issue.

The fact that biometric data is not replaceable and is not secret, combined with the

existence of several types of attacks that are possible in a biometric system, make the

issue of security/integrity of biometric data extremely critical. We introduce two

applications of an amplitude modulation-based watermarking method, in which we

hide a user’s biometric data in a variety of images. This method has the ability to

increase the security of both the hidden biometric data (e.g., eigen-face coefficients)

and host images (e.g., fingerprints). Image adaptive data embedding methods used in

our scheme lead to low visibility of the embedded signal. Feature analysis of host

images guarantees high verification accuracy on watermarked (e.g., fingerprint)

images. The ability of biometrics-based personal identification techniques to

differentiate between an authorized person and an impostor who fraudulently

acquires the access privilege of an authorized person is one of the main reasons for

their popularity compared to traditional identification techniques. However, the

security and integrity of the biometric data itself are important issues. Encryption,

watermarking, and steganography are possible techniques to secure biometric data.

In this paper, two applications of watermarking to secure that data are presented. In

addition to watermarking, encryption can also be used to further increase the security

of biometric data. The first application is related to increasing the security of

biometric data exchange, which is based on steganography. In the second

application, we embed facial information in fingerprint images. In this application,

the data is hidden in such a way that the features that are used in fingerprint

matching are not significantly changed during encoding/decoding. As a consequence,

the verification accuracy based on decoded watermarked images is very similar to

that with original images. The proposed method utilizes several properties of the

50

human visual system to keep the visibility of the changes made to the host image

low. We are currently working on increasing the data hiding capacity of the host

images. Another topic for future research is to investigate how different (e.g., robust

and fragile) watermarking schemes can be combined.

Jain and Uludag [JAI, 2003] suggest the use of steganography principles to hide

biometric data (e.g., fingerprint minutiae) in host images (e.g., faces). This is

particularly useful in distributed systems where the raw biometric data may have to

be transmitted over a non-secure communication channel. Embedding biometric data

in an innocuous host image prevents an eavesdropper from accessing sensitive

template information. The authors also discuss a novel application wherein the facial

features of a user (i.e., eigen-coefficients) are embedded in a host fingerprint image

(of the user). In this scenario, the watermarked fingerprint image of a person may be

stored in a smart card issued to that person. At an access control site, the fingerprint

of the person possessing the card will first be compared with the fingerprint present

in the smart card. The eigen-coefficients hidden in the fingerprint image can then be

used to reconstruct the user’s face thereby serving as a second source of

authentication.

Anil K. Jain,Arun Ross,and Salil Prabhakar [JAI, 2004] in “An Introduction to

Biometric Recognition” says that a wide variety of systems requires reliable personal

recognition schemes to either confirm or determine the identity of an individual

requesting their services. The purpose of such schemes is to ensure that the rendered

services are accessed only by a legitimate user and no one else. Examples of such

applications include secure access to buildings, computer systems, laptops, cellular

phones, and ATMs. In the absence of robust personal recognition schemes, these

systems are vulnerable to the wiles of an impostor. Biometric recognition or, simply,

biometrics refers to the automatic recognition of individuals based on their

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physiological and/or behavioral characteristics. By using biometrics, it is possible to

confirm or establish an individual’s identity based on “who he is,” rather than by

“what he possesses” (e.g., an ID card) or “what he remembers” (e.g., a password). In

this paper, we give a brief overview of the field of biometrics and summarize some

of its advantages, disadvantages, strengths, limitations, and related privacy concerns.

Biometric-based systems also have some limitations that may have adverse

implications for the security of a system. While some of the limitations of biometrics

can be overcome with the evolution of biometric technology and a careful system

design, it is important to understand that foolproof personal recognition systems

simply do not exist and perhaps, never will. Security is a risk management strategy

that identifies, controls, eliminates, or minimizes uncertain events that may adversely

affect system resources and information assets. The security level of a system

depends on the requirements (threat model) of an application and the cost-benefit

analysis. In our opinion, properly implemented biometric systems are effective

deterrents to perpetrators. There are a number of privacy concerns raised about the

use of biometrics. A sound trade-off between security and privacy may be necessary;

collective accountability/acceptability standards can only be enforced through

common legislation. Biometrics provides tools to enforce accountable logs of system

transactions and to protect an individual’s right to privacy. As biometric technology

matures, there will be an increasing interaction among the market, technology, and

the applications. This interaction will be influenced by the added value of the

technology, user acceptance, and the credibility of the service provider. It is too early

to predict where and how biometric technology would evolve and get embedded in

which applications. But it is certain that biometric-based recognition will have a

profound influence on the way we conduct our daily business.

Umut Uludag, Anil K. Jain [ULU, 2004A] in “Attacks on Biometric Systems: A Case

Study in Fingerprints” authors gives an idea that in spite of numerous advantages of

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biometrics based personal authentication systems over traditional security systems

based on token or knowledge, they are vulnerable to attacks that can decrease their

security considerably. In this paper, we analyze these attacks in the realm of a

fingerprint biometric system. We propose an attack system that uses a hill climbing

procedure to synthesize the target minutia templates and evaluate its feasibility with

extensive experimental results conducted on a large fingerprint database. Several

measures that can be utilized to decrease the probability of such attacks and their

ramifications are also presented.

[2004, Anil K. Jain, Sharath Pankanti, Salil Prabhakar, Lin Hong, and Arun Ross

[JAI, 2004D] in “Biometrics: A Grand Challenge” state that Reliable person

identification is an important problem in diverse businesses. Biometrics,

identification based on distinctive personal traits, has the potential to become an

irreplaceable part of any identification system. While successful in some niche

markets, the biometrics technology has not yet delivered its promise of foolproof

automatic identification. With the availability of inexpensive biometric sensors and

computing power, it is becoming increasingly clear that widespread usage of

biometric person identification is being stymied by our lack of understanding of

three fundamental problems: (i) How to accurately and efficiently represent and

recognize biometric patterns? (ii) How to guarantee that the sensed measurements

are not fraudulent? and (iii) How to make sure that the application is indeed

exclusively using pattern recognition for the expressed purpose? Solving these core

problems will not only catapult biometrics into mainstream applications but will also

stimulate adoption of other pattern recognition applications for providing effective

automation of sensitive tasks without jeopardizing our individual freedoms. For these

reasons, we view biometrics as a grand challenge - "a fundamental problem in

science and engineering with broad economic and scientific impact".

53

Arun Ross and Anil Jain [ROS, 2004] in “Biometric Sensor Interoperability: A Case

Study In Fingerprints” The problem of biometric sensor interoperability has received

limited attention in the literature. Most biometric systems operate under the

assumption that the data (viz., images) to be compared are obtained using the same

sensor and, hence, are restricted in their ability to match or compare biometric data

originating from different sensors. Although progress has been made in the

development of common data exchange formats to facilitate the exchange of feature

sets between vendors, very little effort has been invested in the actual development

of algorithms and techniques to match these feature sets. In the Fingerprint

Verification Competition (FVC 2002), for example, the evaluation protocol only

matched images originating from the same sensor although fingerprint data from 3

different commercial sensors was available. This is an indication of the difficulty in

accommodating sensor interoperability in biometric systems. In this paper we discuss

this problem and present a case study involving two different fingerprint sensors.

Almost every biometric indicator is affected by the sensor interoperability problem.

However, no systematic study has been conducted to ascertain its effect on real-

world systems. Normalization at the raw data and feature set levels of a biometric

system may be needed to handle this problem. There is also a definite need to

develop matching algorithms that do not implicitly rely on sensor characteristics to

perform matching. Biometric vendors and independent test groups (e.g., NIST, IBG)

should begin incorporating interoperable scenarios in their testing protocol. This

would help in understanding the effect of changing sensors on a biometric system

and would encourage the development of cross-compatible feature extraction

(representation) and matching algorithms.

Arun Ross and Anil K. Jain [ROS, 2004A] in “MULTIMODAL BIOMETRICS: AN

OVERVIEW” the authors feel that unimodal biometric systems have to contend with

a variety of problems such as noisy data, intra-class variations, restricted degrees of

54

freedom, non-universality, spoof attacks, and unacceptable error rates. Some of these

limitations can be addressed by deploying multimodal biometric systems that

integrate the evidence presented by multiple sources of information. This paper

discusses the various scenarios that are possible in multimodal biometric systems,

the levels of fusion that are plausible and the integration strategies that can be

adopted to consolidate information. We also present several examples of multimodal

systems that have been described in the literature. By combining multiple sources of

information, these systems improve matching performance, increase population

coverage, deter spoofing, and facilitate indexing. Various fusion levels and scenarios

are possible in multimodal systems. Fusion at the match score level is the most

popular due to the ease in accessing and consolidating matching scores. Performance

gain is pronounced when uncorrelated traits are used in a multimodal system.

Incorporating user-specific parameters can further improve performance of these

systems. With the widespread deployment of biometric systems in several civilian

and government applications, it is only a matter of time before multimodal biometric

systems begin to impact the way in which identity is established in the 21st century.

Anil K. Jain, Sarat C. Dass and Karthik Nandakumar [JAI, 2004A] in “Can soft

biometric traits assist user recognition?” states that Biometrics is rapidly gaining

acceptance as the technology that can meet the ever increasing need for security in

critical applications. Biometric systems automatically recognize individuals based on

their physiological and behavioral characteristics. Hence, the fundamental

requirement of any biometric recognition system is a human trait having several

desirable properties like universality, distinctiveness, permanence, collectability,

acceptability, and resistance to circumvention. However, a human characteristic that

possesses all these properties has not yet been identified. As a result, none of the

existing biometric systems provide perfect recognition and there is a scope for

improving the performance of these systems. Although characteristics like gender,

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ethnicity, age, height, weight and eye color are not unique and reliable, they provide

some information about the user. We refer to these characteristics as “soft” biometric

traits and argue that these traits can complement the identity information provided by

the primary biometric identifiers like fingerprint and face. This paper presents the

motivation for utilizing soft biometric information and analyzes how the soft

biometric traits can be automatically extracted and incorporated in the decision

making process of the primary biometric system. Preliminary experiments were

conducted on a fingerprint database of 160 users by synthetically generating soft

biometric traits like gender, ethnicity, and height based on known statistics. The

results show that the use of additional soft biometric user information significantly

improves (apx. 6%) the recognition performance of the fingerprint biometric system.

D. Swangpol and T. Chalidabhongse [SWA, 2005] in “Automatic Person

identification using Multiple Cues” describes a method for vision-based person

identification that can detect, track, and recognize person from video using multiple

cues: height and dressing colors. The method does not require constrained target’s

pose or fully frontal face image to identify the person. First, the system, which is

connected to a pan-tilt-zoom camera, detects target using motion detection and

human cardboard model. The system keeps tracking the moving target while it is

trying to identify whether it is a human and identify who it is among the registered

persons in the database. To segment the moving target from the background scene,

we employ a version of background subtraction technique and some spatial filtering.

Once the target is segmented, we then align the target with the generic human

cardboard model to verify whether the detected target is a human. If the target is

identified as a human, the card board model is also used to segment the body parts to

obtain some salient features such as head, torso, and legs. The whole body silhouette

is also analyzed to obtain the target’s shape information such as height and slimness.

We then use these multiple cues (at present, we uses shirt color, trousers color, and

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body height) to recognize the target using a supervised self-organization process. We

preliminary tested the system on a set of 5 subjects with multiple clothes. The

recognition rate is 100% if the person is wearing the clothes that were learned before.

In case a person wears new dresses the system fail to identify. This means height is

not enough to classify persons. We plan to extend the work by adding more cues

such as skin color, and face recognition by utilizing the zoom capability of the

camera to obtain high resolution view of face; then, evaluate the system with more

subjects.

Anil K. Jain Arun Ross Umut Uludag [JAI, 2005] in “BIOMETRIC TEMPLATE

SECURITY: CHALLENGES AND SOLUTIONS” gives their ideas that a biometric

system is vulnerable to a variety of attacks aimed at undermining the integrity of the

authentication process. These attacks are intended to either circumvent the security

afforded by the system or to deter the normal functioning of the system. We describe

the various threats that can be encountered by a biometric system. We specifically

focus on attacks designed to elicit information about the original biometric data of an

individual from the stored template. A few algorithms presented in the literature are

discussed in this regard. We also examine techniques that can be used to deter or

detect these attacks. Furthermore, we provide experimental results pertaining to a

hybrid system combining biometrics with cryptography, that converts traditional

fingerprint templates into novel cryptographic structures. Cancelable biometrics may

be used to “reset” the biometric template of a user in the event that the user’s

template is compromised. Also, biometric cryptosystems can contribute to template

security by supporting biometric matching in secure cryptographic domains.

Uludag et al. [ULU, 2005] convert fingerprint templates (minutiae data) into point

lists in 2D space, which implicitly hide a given secret (e.g., a 128-bit key). The list

does not reveal the template data, since it is augmented with chaff points to increase

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security. The template data is identified only when matching minutiae data from an

input fingerprints available. The system is observed to operate at a Genuine Accept

Rate (GAR) of 76% with no false accepts on a database comprising of 229 users.

Although several techniques have been proposed to enhance the security of a user’s

template, government regulations will also have to be established in order to address

the issue of template privacy. For example, issues related to the sharing of biometric

templates across agencies (e.g., medical companies and law-enforcement agencies)

and the inferring of personal information about an enrolled user from biometric data

(e.g., “Is this person prone to diabetes?”) have to be countered by establishing an

appropriate legal framework.

Anil K. Jain, Arun Ross, and Sharath Pankanti [JAI 2006] in their paper “Biometrics:

A Tool for Information Security” state that establishing identity is becoming critical

in our vastly interconnected society. Questions such as “Is he really who he claims to

be?,” “Is this person authorized to use this facility?” or “Is he in the watchlist posted

by the government?” are routinely being posed in a variety of scenarios ranging from

issuing a driver’s license to gaining entry into a country. The need for reliable user

authentication techniques has increased in the wake of heightened concerns about

security and rapid advancements in networking, communication, and mobility.

Biometrics, described as the science of recognizing an individual based on his or his

physical or behavioral traits, is beginning to gain acceptance as a legitimate method

for determining an individual’s identity. Biometric systems have now been deployed

in various commercial, civilian, and forensic applications as a means of establishing

identity. In this paper, we provide an overview of biometrics and discuss some of the

salient research issues that need to be addressed for making biometric technology an

effective tool for providing information security. The primary contribution of this

overview includes: 1) examining applications where biometrics can solve issues

pertaining to information security; 2) enumerating the fundamental challenges

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encountered by biometric systems in real-world applications; and 3) discussing

solutions to address the problems of scalability and security in large-scale

authentication systems.

Nalini Ratha, Jonathan Connell, Ruud M. Bolle and Sharat Chikkerur [RAT, 2006]

in “Cancelable Biometrics: A Case Study in Fingerprints” states that Biometrics

offers usability advantages over traditional token and password based authentication

schemes, but raises privacy and security concerns. When compromised, credit cards

and passwords can be revoked or replaced while biometrics are permanently

associated with a user and cannot be replaced. Cancelable biometrics attempt to

solve this by constructing revocable biometric templates. We present several

constructs for cancelable templates using feature domain transformations and

empirically examine their efficacy. We also present a method for accurate

registration which is a key step in building cancelable transforms. The overall

approach has been tested using large databases and our results demonstrate that

without losing much accuracy, we can build a large number of cancelable transforms

for fingerprints.

Raul Sanchez-Reillo , Judith Liu-Jimenez, Michael G. Lorenz, Luis Entrena [REI,

2006] in “Improvement in security evaluation of biometric systems” state that

Security is one of the major issues in IT systems. When users need to be

authenticated, Biometrics appears as an improved alternative to the traditional

systems. If

Biometrics is to be used in an IT product devoted to security, there should be some

kind of assurance that no security holes are added Even more, user biometric data is

a very sensible piece of data. Therefore security achieved shall be evaluated, and a

methodology is needed to cover such evaluation in an objective way. Based on

previous works in security evaluation, such as Common Criteria and ISO, and some

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few initiatives in providing such concepts to the Biometrics world, authors present in

this paper an overview of such works, as well as some proposals to improve Security

Evaluation in Biometrics. This will lead not only to such proposals, but also to some

future works to be carried on in order to complete the work here shown.

Michal Chora´s [CHO, 2007] in “Emerging Methods of Biometrics Human

Identification” state that Human identification biometrics systems may originate

from real-life criminal and forensic applications. Some methods, such as

fingerprinting and face recognition, already proved to be very efficient in computer

vision based human recognition systems. In this paper we focus on emerging

methods of biometrics human identification also used in the forensic and criminal

practice. It is our motivation to design computer vision system based on innovative

computing and image processing that would be used to identify humans on the basis

of their ear, palm and lips images. Those methods have been successfully used by the

police and forensic experts, and now gain attention of computer science community.

In the article we propose innovative methods of ear, lips and palmprint biometrics to

identify humans.

[2007, Haiyong Chen, Hongwei Sun, Kwok-Yan Lam] in “Key Management Using

Biometrics” state that The security of cryptosystems lies in three main factors: the

complexity of the encryption algorithm, the length of the key, and safe storage of the

key. Safe storage of the key, which is known as key management, is the most

vulnerable area in the encryption process. Since biometrics requires the physical

presence of the user, we can use biometric data to protect the cipher key. But the

main obstacle of the integration is that biometric data are noisy while cryptography

requires keys to be exactly right. In this paper, we propose a key management

scheme using biometrics, which can generate constant and accurate keys from noisy

biometric samples every time. The proposed scheme includes key binding and key

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retrieval, utilizing a multivariate linear equation and its solution space to generate

repeatable keys. Our experiments use fingerprint as an instance, and results show

good performance in terms of both accuracy and speed. This scheme can also be

applied to other biometric identifiers such as face ,iris, voice, etc., with most sub-

steps being the same except the generation of false feature elements and the process

of feature matching.

Stephen J. Elliott, Ph.D. Sarah A. Massie [ELL, 2007] “The Perception of Biometric

Technology: A Survey” Their paper outlines the perceptions of 391 individuals on

issues relating to biometric technology. The results from this survey are broadly in

line with other consumer perception surveys conducted in 2003 and 2005, despite the

increase in numbers of applications and deployments that affect individuals.

Noticeably, 25% of the respondents had not heard of biometrics. Those who had

heard of biometrics expressed several concerns, including cleanliness of the devices,

safety (with respect to iris and retinal identification), applications for which their

biometrics were going to be used, and who would have access to their biometric

information. Results demonstrated overwhelming support for biometrics applications

involving law enforcement and obtaining passports, while applications involving

time and attendance tracking and access to public schools ranked lowest on the list.

S.A.Shaikh1, Christos K. Dimitriadis [SHA, 2008] in “My fingers are all mine: Five

reasons why using biometrics may not be a good idea” Biometric technology has

undoubtedly become the bedrock of national and commercial identity management

infrastructures, and will become more so in the future. While the technology

promises great benefits, its use raises a variety of serious ethical, social and technical

concerns. The process-ing and storage of human biological data for this purpose is

not entirely foolproof. Moreover, when it comes to deployment in large-scale

infrastructures, the accuracy and reliability is-sues become more serious.

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Characteristic human data such as facial images and fingerprints is very personal and

permanent to humans, the misuse or abuse of which could be disastrous for the

privacy of individuals. The purpose of this paper is to delve deeper into these issues,

and highlight some of these con-cerns.

S.Asha and C.Chellappan [ASH, 2008] in “Authentication of E-Learners Using

Multimodal Biometric Technology” state that E-learning systems represent a new

form of learning and are becoming more and more popular everyday. Hence security

in e-Learning has become a fundamental requirement. But the problem of e-learning

systems is that only little money is invested for security. Also to authenticate an e-

learner especially in case of e-exams is a major challenge in an e-learning

environment. The role of authentication techniques to prevent unauthorized access

by malicious users becomes more significant. User authentication methods can be

classified into three categories: (1) methods based on human memory such as

passwords, (2) methods based on physical devices such as magnetic or IC cards, and

(3) methods based on biometrics such as fingerprint, iris, etc., As the first two

categories cannot escape vulnerabilities caused by forgetfulness or losses, the third

category attracts much attention these days. A major problem of biometrics

authentication is that it is not free from an error in the process of extraction of human

characteristics and comparison of biometrics data. Multi - biometrics is useful to

improve reliability of biometrics authentication when a single biometrics

authentication technology cannot satisfy a required reliability level. This paper

proposes an authentication system with multi- biometrics to support various services

in e-Learning where user authentication is necessary.

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2.1 Comparison of various biometric technologies

Performance of a biometric measure is usually referred to in terms of the false accept

rate (FAR), the false non match or reject rate (FRR), and the failure to enroll rate

(FTE or FER). The FAR measures the percent of invalid users who are incorrectly

accepted as genuine users, while the FRR measures the percent of valid users who

are rejected as impostors.

In real-world biometric systems the FAR and FRR can typically be traded off against

each other by changing some parameter. One of the most common measures of real-

world biometric systems is the rate at which both accept and reject errors are equal:

the equal error rate (EER), also known as the cross-over error rate (CER). The lower

the EER or CER, the more accurate the system is considered to be.

A number of biometric characteristics may be captured in the first phase of

processing. However, automated capturing and automated comparison with

previously stored data requires that the biometric characteristics satisfy the following

characteristics:

1. Universal: Every person must possess the characteristic/attribute. The

attribute must be one that is universal and seldom lost to accident or disease.

2. Uniqueness: Each expression of the attribute must be unique to the

individual. The characteristics should have sufficient unique properties to

distinguish one person from any other. Height, weight, hair and eye color are

all attributes that are unique assuming a particularly precise measure, but do

not offer enough points of differentiation to be useful for more than

categorizing.

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3. Permanence: They should be constant over a long period of time. The

attribute should not be subject to significant differences based on age either

episodic or chronic disease.

4. Collectability/Measurability: The properties should be suitable for capture

without waiting time and must be easy to gather the attribute data passively.

5. Performance: it is the measurement of accuracy, speed, and robustness of

technology used.

6. Acceptability: The capturing should be possible in a way acceptable to a

large percentage of the population. Excluded are particularly invasive

technologies, i.e. technologies which require a part of the human body to be

taken or which (apparently) impair the human body. Circumvention- Ease of

use of a substitute.

There are also some other parameters which are very important during the analysis of a biometric trait, these are:

Reducibility: The captured data should be capable of being reduced to a file

which is easy to handle.

Reliability and Tamper-resistance: The attribute should be impractical to mask

or manipulate. The process should ensure high reliability and reproducibility.

Privacy: The process should not violate the privacy of the person.

Comparable: Should be able to reduce the attribute to a state that makes it

digitally comparable to others. The less probabilistic the matching involved, the

more authoritative the identification.

Inimitable: The attribute must be irreproducible by other means. The less

reproducible the attribute, the more likely it will be authoritative.

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Among the various biometric technologies being considered, the attributes which

satisfy the above requirements are fingerprint, facial features, hand geometry, voice,

iris, retina, vein patterns, palm print, DNA, keystroke dynamics, ear shape, odor,

signature etc.

Table below shows a comparison of various biometric systems in terms of above

mentioned parameters. A. K. Jain ranks each biometric based on the categories as

being low, medium, or high. A low ranking indicates poor performance in the

evaluation criterion whereas a high ranking indicates a very good performance.

Table 2.1 Comparison of various biometric traits (H=High, M=Medium, L=Low)

Biometrics

Universality

Uniqueness

Permanence

Collect-ability

Performance

Accept-ability

Circumvention

Face H L M H L H L

Fingerprint M H H M H M H Hand geometry M M M H M M M

Keystrokes L L L M L M M

Hand veins M M M M M M H

Iris H H H M H L H

Retinal scan H H M L H L H

Signature L L L H L H L

Voice M L L M L H L

Face H H L H M H H

Odor H H H L L M L

DNA H H H L H L L

Gait M L L H L H M

Ear Canal M M H M M H M

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The use of biometrics in authentication mechanisms is interesting because it has the

possibility to establish the real connection between the physical user and the user's

identity. We always carry our biometrics with us and the loss of keys, tokens and

forgotten passwords would not be the problem any more. Biometrics also possesses

some major problems, some of which are very hard to solve. First, like all

authentication systems, we have a need to store the digital version of the biometrics

(during enrollment). There always exists a way to break a systems security, even that

of the most robust military organization, this leaves us with an abnormal problem

concerned with the storage of biometrics information. If the system is penetrated and

the biometrics- information is stolen or copied, in that case not only the systems

security compromised even the identity of the physical user stolen forever. How can

we change the pattern of our iris, our thumbprint, OR our voice? Moreover

thumbprint the user left two months ago will not be exactly the same as the one he

leaves today. The physical alignment of the biometrics is changing over time. The

thumb will not be positioned in the same way two consecutive times in a row; at

least it is very unlikely. The biometrics might also have changed. Consider that the

user has been working on his summerhouse for the weekend and scratched his

thumb. When he tries to log in to the system the digital copy will not match the

evolved original. A lot of such scenarios can be constructed and many of them are

very likely to happen. In other words biometrics has to deal with false positives and

false negatives. A false positive is when the system authenticates a user even though

he is not “right user”. A false negative is when the system denies the access of a

rightful user. Biometrics will get better at detecting false positives and false

negatives but the technology of today is far from perfect. Another problem with

biometrics is that they can be physically copied. This is possible to do with material

readily available in hobby stores. T. Matsumoto, H. Matsumoto, K. Yamada, S.

Hoshino [MAT, 2002] describes in their paper ”Impact of Artificial Gummy Fingers

on Fingerprint Systems” how to construct a gelatine finger from a thumbprint that

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fools fingerprint detectors about 80% of the time. They takes a fingerprint left on a

piece of glass, enhances it with a cyanoacrylate adhesive, and then photographs it

with a digital camera. Using PhotoShop, he improves the contrast and prints the

fingerprint onto a transparency sheet. Then, he takes a photosensitive printed-circuit

board (PCB) and uses the fingerprint transparency to etch the fingerprint into the

copper, making it three-dimensional. Finally, they makes a gelatine finger using the

print on the PCB. Although not all biometrics can be copied in the same simple

fashion it shows that it is quite possible with a small investment to copy biometrics.

Successful attempts at fooling iris scanners, voice recognition systems etc. have been

made as well. Finally, there exists a fatal practical problem with biometrics. If users

are not willing to use the technology in fear of privacy, getting hurt by the

mechanism, it will never successful. Biometrics can be a promising future method of

authentication for systems that don’t communicate over common infrastructure. If

biometric used remotely over a network we could use a base secret with the same

entropy and get rid of the problems connected with biometrics.

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Chapter 3

Improving Security in Biometric Systems

3.1 Introduction

Biometrics-based personal authentication systems are becoming increasingly

popular, compared to traditional systems that are based on tokens (say key) or

knowledge (say password) [JAI, 1999]. Traditional authentication systems cannot

discriminate between an impostor who fraudulently obtains the access privileges (say

key, password) of a genuine user and the genuine user himself. Further, biometric

authentication systems can be more convenient for the users since there is no

password to be forgotten or key to be lost. In spite their numerous advantages,

biometric systems are also vulnerable to attacks, which can decrease their security.

Ratha et al. [RAT, 2001] analyzed these attacks, and categorized them into eight

different types. Figure 3.1 shows these eight types of attacks (numbered from 1 to 8)

along with the components of a typical biometric system that can be compromised.

Type-1 attack involves presenting a fake biometric (e.g., synthetic fingerprint, face,

iris etc.) to the sensor. Type-2 attack can be achieved by submitting a previously

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intercepted biometric data. Type-3 attack can happen when feature extractor module

is compromised to produce feature values selected by the attacker. Genuine feature

values are replaced with the ones selected by the attacker. In Type-4 attack, the

channel between feature extractor module and matcher is hacked and override by

duplicated data, thus modified the output of feature extractor module. Type-5 attack

is achieved by imposing an artificially high score at matching module. The attack on

the template database (e.g., adding a new template, modifying an existing template,

removing templates, etc.) constitutes the Type-6 attack. The transmission medium

between the template database and matcher is attacked in the Type-7 attack, resulting

in the alteration of the transmitted templates. Finally, the matcher result (accept or

reject) can be overridden by the attacker that is Type-8 attack.

Schneier [SCH, 1999] compares traditional security systems with biometric systems.

Schneier said that though biometrics are known as uniquely identifiers, but they are

not secrets. We leave our fingerprints on everything we touch, and our iris patterns

can be observed anywhere we look. Biometrics can not handle failure also in a very

well way. Imagine that Mr. Sumit is using his thumbprint as a biometric, and

someone steals the digital file. This is not like a security certificate, where Mr. Sumit

can get another one. Any human being has only two thumbs. Once someone steals

any of our biometric, it remains stolen for whole life and there is no getting back to a

secure situation. Biometric keys are necessarily common across different functions.

Just as we should never use the same password on two different systems, the same

encryption key should not be used for two different applications. If my fingerprint is

used to start my car, unlock my medical records, and read my electronic mail, then

there may be chance of arising any insecure situations. Biometrics are powerful and

useful, but they are not keys. They are not useful when you need the characteristics

of a key: secrecy, randomness, the ability to update or destroy. They are useful as a

replacement for a PIN, or a replacement for a signature (which is also a biometric).

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They can sometimes be used as passwords: a user can't choose a weak biometric in

the same way they choose a weak password.

In Denial of Service (DoS), an attacker corrupts the authentication system so that

legitimate users cannot use it. For a biometric authentication system, an online

authentication server that processes access requests (via retrieving templates from a

database and performing matching with the transferred biometric data) can be

bombarded with many bogus access requests, to a point where the server’s

computational resources cannot handle valid requests any more.

In circumvention, an attacker gains access to the system protected by the

authentication application.

Figure3.1. Eight different attack points in a biometric authentication system.

This threat can be cast as a privacy attack, where the attacker accesses the data that

he was not authorized (e.g., accessing the medical records of another user) or, as a

subversive attack, where the attacker manipulates the system (e.g., changing those

records, submitting bogus insurance claims, etc.).

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In repudiation, the attacker denies accessing the system. For example, a corrupt bank

clerk who modifies some financial records illegally may claim that his biometric data

was “stolen”, or he can argue that the False Accept Rate (FAR) phenomenon

associated with any biometric may have been the cause of the problem.

In contamination (covert acquisition), an attacker can surreptitiously obtain

biometric data of legitimate users (e.g., lifting a latent fingerprint and constructing a

three-dimensional mold) and use it to access the system. Further, the biometric data

associated with a specific application can be used in another unintended application

(e.g., using a fingerprint for accessing medical records instead of the intended use of

office door access control). This becomes especially important for biometric systems

since we have a limited number of useful biometric traits, compared to practically

unlimited number of traditional access identities (e.g., keys and passwords). Cross-

application usage of biometric data becomes more probable with the growing

number of applications using biometrics (e.g., opening car or office doors, accessing

bank accounts, accessing medical records, locking computer screens, gaining travel

authorization, etc.).

In collusion, a legitimate user with wide access privileges (e.g., system

administrator) is the attacker who illegally modifies the system.

In coercion, attackers force the legitimate users to access the system (e.g., using a

fingerprint to access ATM accounts at a gunpoint) [MAL, 2003].

The problems that may arise from the above mentioned attacks on biometric systems

are raising concerns as more and more biometric systems are being deployed both

commercially and in government applications. This, along with the increase in the

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size of the population using these systems and the expanding application areas (visa,

border control, health care, welfare distribution, e-commerce, etc.) may lead to

possible finance, privacy, and security related breaches.

These days, biometric technologies are typically used to analyze human

characteristics for security purposes. There is explosion of information exchange and

sensitive data across the network or internet and as a result cryptography is becoming

an increasingly important feature of computer security. Cryptography, concerns itself

with the projection of trust i.e. with taking trust from where it exists to where it is

needed. A strong combination of biometrics and cryptography might, for example,

have the potential to make the existing system more secure. Many cryptographic

algorithms are available for securing information. However, the security is

dependent on the secrecy of the secret key because larger the size of key, stronger

will be security. But it would increasingly be difficult to remember the user so big

key.

Here an approach is presented where the user is not bothered to remember any key

because his biometrics traits will work as secret key. The biometrics traits e.g.

fingerprint, hand, eye, face, and voice encrypt with original message to generate the

encrypted data and further the same will be used to decrypt it. In traditional

cryptosystems, user authentication is based on possession of secret keys. The method

fails if the keys are not kept secret (i.e., shared with non-legitimate users). Further,

keys can be forgotten, lost, or stolen and, thus, cannot provide non-repudiation.

Current authentication systems based on physiological and behavioral characteristics

of persons (known as biometrics), such as fingerprints, inherently provide solutions

to many of these problems and may replace the authentication component of

traditional cryptosystems. Various methods have been achieved that bind a

cryptographic key with the biometric template of a user stored in the database in

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such a way that the key cannot be revealed without a successful biometric

authentication. We assess the performance of one of these biometric key

binding/generation algorithms using the fingerprint biometric. We illustrate the

challenges involved in biometric key generation primarily due to sweeping

acquisition variations in the representation of a biometric identifier and the imperfect

nature of biometric feature extraction and matching algorithms.

3.1.1 Threat Categorization in Biometric Security Systems

There are various types of attacks to a biometric security system as discussed below:

(i) Insider or software/physical/electrical attacks: As with any IT security system,

biometric-based security policy must deal with the threats from the workers of the

organization who can damage any software or hardware component of the system.

Attackers may also change the statistical recognition parameters of the components

and decrease the recognition rates.

(ii) Attacks on the biometric sensor: Examples of such attacks are spoofing biometric

features by presented artificial biometric to the device, which get accepted by the

sensor.

(iii) Attacking on reference data: This only leads to loss of privacy for the respective

person or to further attacks, if the intercepted data is subsequently actively re-

introduced to the subsystem.

(iv) Channel attacks: These kinds of attacks are called man-in-the-middle attacks. If

the channel between the sensor and the feature extraction unit or the one between the

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reference database and the matching unit is attacked, the attacker will gain

information about the biometric data.

The rest of the chapter has been divided into the following three sections and

presents the approaches, developed by the author of this thesis to improving the

security of biometrics system by addressing the problems of attack on biometric

device.

Section 3.2 deals with the concept of cryptography. Biometrics are widely used in

person identification and verification. But using biometrics by means of

cryptography is a new research area and there is less work reported in this area. Here

we are giving a brief explanation of biometric cryptography approaches and

algorithms which use various biometric data. Biometric Encryption technique [SOU,

1998] is a two stage process to authenticate the user. During the enrollment phase,

the process combines the biometric image with a digital key to create a secure block

of data known as BioScrypt and then the key is retrieved using the biometric during

the verification phase. Note that the key is completely independent of the biometric

data so that the use of the biometric is not forfeited if the key is ever compromised

and hence can be easily modified or updated.

Section 3.3 gives solution to make the database templates more secure using

steganography. Steganography today, is significantly more sophisticated method for

security purpose. It allows a user to hide large amounts of information within images

and audio files. These forms of steganography often are used in conjunction with

cryptography so that the information is doubly protected. First it is encrypted and

then hidden so that an adversary has to first find the information and then decrypt it.

Steganography can also be used to allow communication within an underground

community. There are several reports, for example, of persecuted religious minorities

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using steganography to embed messages for the group within images that are posted

to known web-sites.

Section 3.4 describes the problem, when the biometric of a user has been

compromised. No doubt biometrics offers various advantages over traditional token

and password based authentication schemes, but raises privacy and security

concerns. The credit cards, passwords, token can be revoked or replaced, when

compromised, while biometrics are permanently associated with a user and cannot be

replaced. Cancelable biometrics solves this problem by constructing revocable

biometric templates. We present several constructs for cancelable templates using

feature domain transformations by applying some hashing functions. We have also

presented a method for in building mutants of actual templates by cancelable

transforms. We can build a large number of cancelable transforms for fingerprints

and other biometric traits also.

3.2 Hiding Data Using Biometric Cryptography

Biometric characteristics of an individual could be used for different purposes.

Biometrics are widely used in identification and verification. The fusions of the

results of various biometric technologies are used to improve recognition

performance. These kinds of systems are called multimodal biometric person

identification systems. An example which has a wide variety of applications is the

speaker identification problem where basically two sources of information exist:

audio signal (voice) and video signal. Speaker identification, when performed over

audio streams, is probably one of the most natural ways to perform person

identification. However, video stream is also an important source of biometric

information, in which we have still images of biometric features such as face and

also the temporal motion information such as lip movement, which is correlated with

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the audio stream. The reliability and robustness of such a system could be increased

by applying fusion techniques. The most important issue in a multimodal scheme is

how to fuse different biometric signals. To do this, two possible strategies are

possible (i) early integration (ii) late integration. In early integration, modalities are

fused at data or feature level and in late integration they are fused at decisions or

score level. When more than two modalities are available, a better alternative that

has been addressed in this thesis is to make use of both strategies, i.e. to employ

early integration and/or late integration where appropriate for bimodal fusion of

different modality couples.

There are various methods that can be deployed to secure a key with a biometric.

First one involves remote template matching and key storage. In this method

biometric image is captured and compared with a corresponding template. If the user

is verified, the key is released. The main problem here is using an insecure storage

media. Second method hides the cryptographic key within the enrollment template

itself via a secret bit-replacement algorithm. When the user is successfully

authenticated, this algorithm extracts the key bits from the appropriate locations and

releases the key. The drawback of this scheme is that the key will be retrieved from

the same location in a template each time a different user is authenticated. Using data

that is derived directly from a biometric image can be another method. In this

manner biometric templates are used as a cryptographic key. But sensitivities due to

environmental and physiological factors and compromising of the cryptographic

keys stand as a big obstacle.

3.2.1 Problems to Generate Biometric Cryptographic Key

There are several problems which must be addressed in order to generate a useful

biometric cryptographic key [ULU, 2004]. Ensuing paragraphs are discussing those

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problems which are associated with (i) the entropy (strength) of the biometric key,

(ii) uniqueness of biometric key, and the (iii) stability of the biometric key.

(i) Key Entropy (strength). Instead of developing simply longer cryptographic keys

to resist brute force attacks, a more intelligent approach might be to aggregate

features and parameters from an individual in such a way that their correlation

generates a key that is much stronger than the individual size of the actual key.

(ii) Key Uniqueness. The uniqueness of a biometric key will be determined by the

uniqueness of the individual biometric characteristics used in the key. Instead of

trying to find a single unique feature, biometric key needs to find only a collection of

somewhat unique features or parameters that when assembled collectively create a

unique profile for an individual.

(iii) Key Stability. A major problem with biometric identification is that individual’s

enrollment template and sample template, which can vary from session to session.

This variation can occur for a number of reasons including different environments

(e.g. lighting, orientation, emotional state) or physical changes (e.g. facial hair,

glasses, cuts). If a set of relatively stable features can be determined and the amount

of variation can be reduced to an acceptable number of bits, then it might be possible

for a valid user to search a limited key space to recover an encrypted transmission

while making a brute force search by an attacker remain difficult if not impossible.

3.2.2 Methods to Secure Biometric Key

There are various methods that can be deployed to secure a key with a biometric

[CLA, 2003]. Out of those two methods are discussed here, which are more

concerned to this work.

One method that involves remote template matching and key storage is discussed

below. The biometric image is captured and the corresponding template is sent to a

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secure location for template comparison. If the user is verified, then the key is

released from the secure location. In this scenario, the communication line must also

be secured to avoid spoof attacks.

Second method that involves hiding the cryptographic key within the enrollment

template itself via a trusted (secret) bit-replacement algorithm [LIN, 2003]. Upon

successful authentication by the user, this trusted algorithm would simply extract the

key bits from the appropriate locations and release the key into the system.

3.2.3 Existing System: Traditional Cryptography

Figure3.2 below shows the functionality of an existing cryptographic system, where

Encryption and Decryption depends on secret key. In First phase, the cipher text is

generated through encryption and in second phase, the cipher text got decrypted to

get original plain text. With the traditional cryptographic system, the following

problems may arise.

(i) Key Entropy (strength)

(ii) Key Uniqueness

(iii)Key Stability

To address these problems, the author of this thesis has proposed a method [KAN,

2007], which is discussed in the next paragraphs.

Figure3.2. Traditional cryptography system

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3.2.4 Proposed System: Biometric Cryptography

Encryption and decryption process proposed by the author of this thesis [KAN,

2007] are shown in figure 3.3 and 3.4 respectively. The encryption process (as

shown in Figure 3.4) takes the input as biometric template and random generated

key. The decryption process (as shown in Figure 3.5) is similar to the encryption

process except that now the template is used to retrieve to secret key using algorithm.

The following sub-sections 3.2.6 and 3.2.7 explain the process of enrollment and

verification.

3.2.5 Enrollment / Verification Process The steps involved in encryption process are described below:

First an overview of the processes of enrollment and verification in reference to

figures 3.3 and figure 3.4 is prescribed below.

Figure 3.3 Enrollment process for Biometric Encryption

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Role of Cryptography during Enrollment Process:

Step1. Capture Biometric Image.

Step2. Generate template for corresponding Biometric Image.

Step3. Generate a random key for binding purpose.

Step4. Bind the Biometric template with random generated key.

Step5. Generate a new key Template using binding algorithm.

Figure 3.4 Decryption process for Biometric at verification end

Figure 3.4 shows the decryption process for biometric at verification end. The steps

involved in verification process are described below:

Role of Cryptography during Verification Process:

Step1. Produce fresh Biometric Image.

Step2. Generate template for corresponding Biometric Image.

Step3. Use previous generated random key for binding purpose.

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Step4. Bind the Biometric template with random generated key using same

algorithm.

Step5. The Key Template has been generated for verification purpose.

3.2.6 Biometric Encryption using other Biometric Templates

Although the Biometric Encryption algorithm was developed primarily for use with

image-based biometric templates, the process can also be applied to other biometric

templates [BIO, 2001]. This can be achieved simply by representing the non image-

based biometric template as an image array. For example, a minutiae-based

fingerprint template can be represented as an image array by embedding a code

referring to each minutiae type at the appropriate location in a two-dimensional

array, thereby, creating a map of the minutiae points. This array can then be input to

the Biometric Encryption algorithm, as described above for fingerprint images [JAI,

2003].

Using a minutiae-based rather than an image-based template may have the added

advantage of producing a rotation invariant system, assuming the original minutiae

template contained information about the relative orientation of the minutiae. For

some other biometric types, different considerations may modify the algorithm. For

example, images of the iris or retina can easily be aligned using the center of the

eye’s pupil as a reference point. Thus, for these types of images, the Biometric

Encryption process is not required to be translation invariant. Therefore, transforms

other than the Fourier transform may be appropriate, such as the Gabor transform,

which was originally used in the algorithm for iris identification developed by

Daugman [ROS, 2005]. Also, the distortion tolerance requirements of the filter

function may be relaxed for biometrics other than fingerprints. The majority of the

distortions present in fingerprint images is due to the skin deforming on contact with

a glass or metal surface. For other biometrics, such as the iris or retina, there

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typically is no direct contact between the biometric and the system. Therefore, less

distortion will be present in these biometric images.

3.2.7 Advantages of Biometric Encryption Biometrics Encryption Technologies can enhance both privacy and security as

discussed below:

(i) No preservation of biometric image or template: there is no need to carry or

remember the biometric templates. These are by defaults exists in our body and

always remains with us.

(ii) Multiple / cancelable / revocable identifiers: In case of hacking a template, one

can issue another template of same biometric to the user by use of cancelable

biometric technique. The author of this thesis has presented an approach [KAN,

2009] based on cancelable biometric and discussed in section 3.4. (iii) Improved security of personal data and communications: No doubt the security

has increased by the use of biometric encryption, as biometric can not be stolen or

guessed.

(iv) Greater public confidence, acceptance, and use; greater compliance with privacy

& data protection laws.

(v) Suitable for large-scale applications: Biometrics applications are very much

useful in large scale industries or institutions, where enrollment, identification and

verification should be automated.

(vi) Improved authentication security:

• No need for user memorization.

• Less susceptible to security attacks.

• No substitution attack: nobody knows the key and cannot create a fake

template.

• No tampering: biometric features aren’t stored.

• No Trojan horse attacks: no score is used.

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• No overriding Yes/No response.

• More resilient to a masquerade attack.

The next approach to improve the security in a biometric system, developed by the

author of this thesis is based on steganography (hiding secret information in picture)

and is discussed in section below.

3.3 Hiding Biometric data using Steganography

Steganography is a technique to hide the message in digital objects such as image,

video, music or any other computer file. The idea was first described by Simmons in

1983 [SIM, 1983]. More comprehensive theory of steganography is given in [AND,

1996]. Steganography is different from cryptography, which is about concealing the

contents of message. Steganography is about concealing the existence of message

itself [AND, 1996].

Digital images are more attractive for steganography purpose since these contain a

significant amount of data and can be modified slightly without leading to visible

artifacts [EGG, 2002]. Digital images can be expressed as collection of pixel values

at each pixel position. In gray scale images each pixel is represented by one byte

from range 00000000(Decimal value 0) to 11111111(Decimal value 255). Here pixel

value 0 means black and pixel value 255 means white. In 24 bit color images each

pixel is represented by 3 bytes, one byte each for primary color red, green and blue.

So a combination of red, green and blue color values, which are 0 to 255 for each

color, makes a color at that pixel.

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One of the popular and oldest techniques to hide the message in digital image is to

hide it in least significant bit (LSB) of pixel value. More detail regarding image files

and least significant bit insertion is given in [JOH, 1998].

Traditionally in steganography, the goal is to hide a secret message, which is

transmitted between two or more communication partners in a way to ensure

confidentiality and integrity. The more significant goals in the context of documents

are integrity and authenticity and here techniques derived from steganography are

digital watermarks, and is outlined in the following subsection. There are a large

number of steganographic methods that have been reported in the literature [KHO,

2008], ranging from invisible ink and microdots to secreting a hidden message in the

second letter of each word of a large body of text and spread spectrum radio

communication. With the advent of computers and networks, many other ways of

hiding information, have been developed:

• Covert channels (e.g., Loki and some distributed denial-of-service tools use

the Internet Control Message Protocol (ICMP) as the communications

channel between the "bad guy" and a compromised system).

• Hidden text within Web pages: Text can be hide in the form of pixel

information in a simple web-page.

• Null ciphers (e.g., using the first letter of each word to form a hidden

message in an otherwise innocuous text).

3.3.1 Steganographic Methods

The following formula provides a very generic description of the pieces of the

steganographic process:

stego_medium = cover_medium + hidden_data + stego_key

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In this context, the cover_medium is the file in which we will hide the hidden_data,

which may also be encrypted using the stego_key. The resultant file is the

stego_medium (which will, of course. be the same type of file as the cover_medium).

The cover_medium (and, thus, the stego_medium) are typically image or audio files.

The authors of the thesis have used image files, and hence cover_medium and

stego_medium here after will be referred as cover_image and stego_image.

Before discussing how information is hidden in an image file, it is useful to review,

how images are stored in the first place. An image file is merely a binary file

containing a binary representation of the color or light intensity of each picture

element (pixel) comprising the image.

Images typically use either 8-bit or 24-bit color. When using 8-bit color, there is a

definition of up to 256 colors forming a palette for this image, each color denoted by

an 8-bit value. A 24-bit color scheme, as the term suggests, uses 24 bits per pixel and

provides a very large set of colors. In this case, each pixel is represented by three

bytes, each byte representing the intensity of the three primary colors Red, Green,

and Blue (RGB), respectively. The Hypertext Markup Language (HTML) format for

indicating colors in a web page often uses a 24-bit format employing six

hexadecimal digits, each pair representing the amount of red, blue, and green,

respectively. The color orange, for example, would be displayed with red set to

100% (decimal 255, hex FF), green set to 50% (decimal 127, hex 7F), and no blue

(0), so the number "#FF7F00" would indicate Orange color in the HTML code.

The size of an image file, then, is directly related to the number of pixels and the

granularity of the color definition. A typical 640 X 480 pixel image using a palette of

256 colors would require a file about 307 KB in size (640 X 480 bytes), whereas a

1024 X 768 pixel high-resolution 24-bit color image would result in a 2.36 MB file

(1024 X 768 X 3 bytes).

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To avoid sending files of this enormous size, a number of compression schemes have

been developed over time such as Bitmap Image (BMP), Graphic Interchange

Format (GIF) and Joint Photographic Experts Group (JPEG) file types. From

steganography point of view, all the compression schemes mentioned above are not

equally suited.

GIF and 8-bit BMP files employ lossless compression, is the scheme allows the

software to exactly reconstruct the original image. JPEG, on the other hand, uses

lossy compression, which means that the expanded image is very nearly the same as

the original but not an exact duplicate. While both methods allow computers to save

storage space, lossless compression is much better suited to applications where the

integrity of the original information must be maintained, such as steganography.

3.3.2 LSB Insertion method of Steganography

The simplest approach to hiding data within an image file is called least significant

bit (LSB) insertion. In this method, one can take the binary representation of the

hidden_data and overwrite the LSB of each byte within the cover_image. In 24-bit

color, the amount of change will be minimal and indiscernible to the human eye. As

an example, suppose that there are three adjacent pixels (nine bytes) with the

following RGB encoding:

11110101 11001101 10101001

10100110 11001111 11001010

10101111 00010011 11001000

Now suppose we want to "hide" the following 9 bits of data (the hidden data is

usually compressed prior to being hidden): 101101101. If we overlay these 9 bits

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over the LSB of the 9 bytes above, we get the following (where bits in bold have

been changed):

11110101 11001100 10101001

10100111 11001110 11001011

10101111 00010011 11001000

Note that we have successfully hidden 9 bits but at a cost of only changing 4 bits i.e.

roughly 50%, of the 9 LSBs.

Similar methods can be applied to 8-bit color but the changes are more dramatic.

Gray-scale images, too, are very useful for steganographic purposes. One potential

problem with any of these methods is that they can be found by an adversary who is

looking. In addition, there are other methods besides LSB insertion with which to

insert hidden information.

The reverse process of steganography is called steganalysis, i.e. art of detecting and

breaking steganography. One technique of this analysis is to examine the color

palette of a graphical image. In most images, there will be a unique binary encoding

of each individual color. If the image contains hidden data, many colors in the palette

will have duplicate binary encodings since, for all practical purposes, one can't count

the LSB, if the analysis of the color palette of a given file yields many duplicates;

one can safely conclude that the file has hidden information.

But now question is, what files would one analyze? Suppose one decides to post a

hidden message by hiding it in an image file that is posted at an auction site on the

Internet. The item being auctioned is real so a lot of people may access the site and

download the file. Only a few people know that the image has special information

that only they can read it. Indeed, the quantity of potential cover files makes

steganalysis a Herculean task.

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3.3.3 Limitations of LSB insertion

In spite of the two advantages of LSB insertion method viz:

(i) if message bit is same as the pixel’s least significant bit then no change at all is

required for that pixel value.

(ii) if pixel value is different from message bit then effective change in pixel value is

either +1 or –1. The +1 or –1 change in pixel value is invisible to human eye.

Many limitations of this method have been found and are discussed below:

First, the message can be easily removed by unauthorized person (intruder) as

message is in least significant bit.

Second disadvantage is more hazardous as message is hidden in least significant bit

so intruder can modify the least significant bits of all the image pixels.

In this way the hidden message can be destroyed, but the change in image quality is

in the range of +1 to –1 at each pixel position, which is negligible to human eye.

Third disadvantage is due to imaging system as the least significant bit is corrupted

by hardware imperfections or quantization noise [SCH, 1994], so the message can be

distorted in this way also.

The first disadvantage is overcome by encrypting the message and distributing it at

random locations. The author of this thesis has developed a method [KAN, 2008] to

address the second limitation. So far in literature no research seems to be reported

which overcomes the second disadvantage of least significant bit insertion by

maintaining both of the advantages of least significant bit. The third disadvantage

has no significance in our technique as we are not using least significant bit for

insertion or retrieval.

Considering MSB as 7th bit and LSB as 0th bit ,our proposed technique make use of

1st and 2nd bit of a randomly chosen pixel value of image to insert the message bit in

such a manner that effective change in pixel value is in the range of +1 to –1. Hence

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image quality is same as in the case of least significant bit insertion, but it is more

robust as second and third disadvantages are overcome too much extent. To avoid

the pattern recognition and get more robustness each message bit is inserted four

times i.e. as 00, 01, 10, and 11 at 1st and 2nd bit position. For this four algorithms are

developed. First algorithm for insertion of 0 and 1 as 00 and 11 respectively. Here

message bit ‘b’ becomes ‘bb’. Second algorithm for insertion of 0 and 1 as 01 and 10

respectively. Here message bit ‘b’ becomes ‘b b’. Third algorithm for insertion of 0

and 1 as 10 and 01 respectively. Forth algorithm for insertion of 0 and 1 as 11 and 00

respectively. All the algorithms use separate secret key to find the random location

for insertion. In our scheme distinct (not repeated) locations are used with in the

same algorithm as well as in different algorithms for insertion.

3.3.4 Steganography in Biometrics This sub-section describes a method developed by the author of this thesis [KAN,

2008], to hide the biometric template using steganography.

Figure 3.5 Use of Steganography for creating secret Templates

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In many cases, the appropriate use of cryptography can reduce these threats. The

Security administrator configures the biometric system to encrypt and digitally sign

all biometric data before it is transmitted from one physical device to another.

Steganography can greatly reduce these attacks because attackers must have to

obtain the system’s private data in addition to breaching the security of the captured

device or biometric storage. This makes these attacks considerably more difficult to

achieve. But steganography is more secure than cryptography because there is no

separate key in steganography rather key is inbuilt in the template [ULU, 2004]. In

Figure 3.5, a scheme has been shown to generate the secret templates with the use of

steganography. Here instead of remembering key (Like in cryptography), the secret

key is mixed in the original templates. The secret key affects the corresponding pixel

color or intensity in the original biometric template and generates a secret template

as shown in Figure 3.5. This secret template is quite more secure as compared to the

original templates in database, because the secret key is also embedding in it with the

help of binding algorithm. The hacker will not be able to break the system even he

know both the secret templates and secret key because he don’t know, how the key

and templates are mixed with binding algorithm? During identification/verification

the authenticate user supplies his biometric data as well as the secret key to gain the

access to the system. The section 3.3.5 describes the method of hiding information in

an image [SIN, 2004].

3.3.5 How to Apply Steganography in Biometrics

Algorithm for insertion of message bit ‘b’.

(i) Find pseudo-random location ‘l’ in an image from the secret key to insert the

message bit b.

(ii) Check whether at location ‘l’, pixel value is 00000000 or 11111111, called

boundary values. If yes, ignore this location and go to step (i). Here we are

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ignoring these boundary values because the change may be +2 or -2 in pixel

values, which is to be avoided.

(iii) Check whether at location ‘l’

a) 6th and 7th bits are b, b? If yes, then no change at ‘l’ is required. Message bit is

already there. Go to End.

b) 6th and 7th bit are b, b or b, b? If yes, then see that whether it is possible to

make 6th and 7th bits as b, b by adding or subtracting 1 to pixel value?. If yes,

do it and go to End. Otherwise ignore the location ‘l’ and go to step (i).

c) 6th and 7th bits are b, b? If yes, then see whether it is possible to make 6th and

7th bits to b, b by adding or subtracting 1? If yes, do it and go to End.

Otherwise change them to b, b or b, b by adding or subtracting 1 and go to (i).

(iv) End.

Algorithm for retrieval of message bit ‘b’

(i) Trace out the location ‘l’ from the same secret key as used in insertion algorithm.

(ii) Pixel value is equal to one of the boundary values, i.e., 00000000 or 11111111?

If yes, then it is invalid address. Go to step (i).

(iii) Check whether at location ‘l’

a) 6th and 7th bits are different, i.e., b, b or b, b? If yes, then it is invalid address

go to step (i).

b) 6th and 7th bits are same i.e. b, b then b is the message bit.

(iv) End.

3.3.6 What happens to pixel value during insertion?

The Table 1 and Table 2 illustrate the working of the first algorithm for insertion of bits 0

and 1 respectively in image pixel value.

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Table 3.1

Decimal Value

Pixel Value before Insertion

Pixel Value after Insertion of 0

Change in Pixel Value and Comment for Insertion of 0

0 00000000 00000000 BV*,NC**, ignore***

1 00000001 00000001 NC, insert**** 2 00000010 00000001 -1*****, insert 3 00000011 00000011 NC, ignore 4 00000100 00000100 NC, ignore 5 00000101 00000101 NC, ignore 6 00000110 00000101 -1, ignore 7 00000111 00001000 +1******, insert 8 00001000 00001000 NC, insert ~ ~ ~ ~ 15 00001111 00010000 +1, insert 16 00010000 00010000 NC, insert ~ ~ ~ ~ 31 00011111 00100000 +1. insert 32 00100000 00100000 NC, insert ~ ~ ~ ~ 63 00111111 01000000 +1, insert 64 01000000 01000000 NC, insert ~ ~ ~ ~ 127 01111111 10000000 +1, insert 128 10000000 10000000 NC, insert ~ ~ ~ ~ 255 11111111 11111111 BV,NC, ignore

*BV– Boundary Value (0 or 255) **NC- No Change in Pixel Value

***ignore– Location is ignored at this Pixel Value

****insert- Message bit is inserted at this Pixel Value

***** -1– Pixel Value is decreased by 1 ******+1– Pixel Value is increased by 1

92

Table 3.2

Decimal Value

Pixel Value before Insertion

Pixel Value after Insertion of 1

Change in Pixel Value and Comment for Insertion of 1

0 00000000 00000000 BV,NC, ignore 1 00000001 00000010 +1, ignore 2 00000010 00000010 NC, ignore 3 00000011 00000011 NC, ignore 4 00000100 00000100 NC, ignore 5 00000101 00000110 +1, insert 6 00000110 00000110 NC, insert 7 00000111 00000111 NC, insert 8 00001000 00000111 -1, insert ~ ~ ~ ~ 15 00001111 00001111 NC, insert 16 00010000 00001111 -1, insert ~ ~ ~ ~ 31 00011111 00011111 NC, insert 32 00100000 00011111 -1, insert ~ ~ ~ ~ 63 00111111 00111111 NC, insert 64 01000000 00111111 -1, insert ~ ~ ~ ~ 127 01111111 01111111 NC, insert 128 10000000 01111111 -1, insert ~ ~ ~ ~ 255 11111111 11111111 BV,NC, ignore

The main results that have been obtained from the insertion algorithm are

summarized below:

• 49% chances, that message bit will be inserted at pseudorandom location at first chance.

• 50% chance, that when message bit is inserted, no change in pixel value is required.

• 12.5% chances, that change in pixel value is required, when we are ignoring the location.

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By using the algorithm discussed above, one can hide the secret data in the host

image and generate a secret template. Steganography plays an important role to

enhance the integrity and security of biometric templates. Biometric cryptosystems

can also contribute to template security (as discussed in section 3.2) by supporting

biometric matching in secure cryptographic domains. In case of cryptography the

‘key’ can be guessed sometime easily but steganography has no separate key to be

hacked, the key is embedded in the template itself. Further, when the amount of

available memory increases, there is a tendency to store more information in the

template. This increases the risks associated with template misuse. As a result, the

issue of template security and integrity continues to cause several challenges, and it

is necessary that further research be conducted in this direction.

3.4 Securing Biometric data using Cancelable

Biometric

With the widespread deployment of biometric systems in various applications, there

are increasing concerns about the security and privacy of biometric technology.

Although many of biometric techniques can be applied to enhance security, even

more protection tools are required to keep safe a given biometric system at various

attack points, for example an attacker may steal templates from a database and

constructs artificial biometrics that breach authentication. It indicates that the

biometrics templates are not secure. A solution should be devised for template-

protection, which can make it impossible to recover the actual biometric data from

the templates.

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Some of the possible solutions have already been discussed in Sections 3.2 and

Section 3.3. One of the properties that make biometrics so attractive for

authentication purposes is their invariance over time, which is also one of its

liabilities. When a credit card number is compromised, the issuing bank can just

assign the customer a new credit card number. When the biometric data are

compromised, replacement is not possible.

In order to alleviate this problem, here comes the concept of “cancelable biometrics”

is introduced. It consists of an intentional, repeatable distortion of a biometric signal

based on a chosen transform. The biometric signal is distorted in the same fashion at

each presentation, for enrollment and for every authentication. With this approach,

every instance of enrollment can use a different transform thus rendering cross-

matching impossible. Furthermore, if one variant of the transformed biometric data is

compromised, then the transform-function can simply be changed to create a new

variant (transformed representation) for re-enrollment as a new person. In general,

the distortion transforms are selected to be noninvertible. So even if the transform

function is known and the resulting transformed biometric data are known, the

original (undistorted) biometrics cannot be recovered.

A biometric ID is a distinguishing feature of the human body that can be used for

authentication e.g. fingerprints eyes, face, hand, voice, and signature. One can say a

biometric links the authenticator to the owner, unlike a password, which can be lent

or stolen. Biometrics are classified further into two subtypes: physical and

behavioral. Physical biometrics are based on body features like fingerprints, hand-

geometry, iris, retina etc. Behavioral biometrics are based on learned gestures such

as signatures, voice, gait, keystroke etc. Biometric authentication is vulnerable to

machine error. For instance, verification error can occur when a one-to-one match is

attempted and identification error can occur when a one-to-many match is attempted.

When error is there consequently false non-match rates (FRR) and false match rates

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(FAR) will be raised. User authentication is part of the larger security system, which

can be strong or weak.

3.4.1 Cancelable Biometrics This is a method of enhancing the security and privacy of biometric authentication.

Instead of enrolling with a true finger (or other biometric), the fingerprint is

intentionally distorted in a repeatable manner and this new print is used. If, for some

reason, the old fingerprint is stolen then an essentially a new fingerprint can be

issued by simply changing the parameters of the distortion process. This also results

in enhanced privacy for the user, since the true fingerprint is never used anywhere

and also different distortions can be used for different types of accounts. The same

technique can also be used with other biometrics to achieve similar benefits. Figure

3.6 shows the result of cancelable process on the face. The above faces are original

and below are the face, after applying cancelable process.

Figure 3.6 Image morphing using Cancelable Biometric

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Putte and Keuning [PUT, 2000] tested several fingerprint sensors to check whether

they accept an artificially created finger instead of a real finger. They describe

methods to create dummy fingers with and without the cooperation of the real owner

of the biometric. When the owner cooperates, obviously the quality of the produced

dummy fingers can be higher than those produced without cooperation. In this

process the plaster cast of the finger, liquid silicon rubber is filled inside the cast to

create a wafer-thin dummy that can be attached to a finger, without being noticed at

all.

Synthetic images are input to the matching algorithm, which in turn handles

conversion of the images into any suitable representation before matching. But, for a

fingerprint-based biometric system, such an approach presents challenges that are not

found in a face-based system. The discriminating information in fingerprints is not

tied to specific geometrical relationships, as it is in face-based systems (e.g., between

eyes, nose, mouth, etc.) and methods that are inherently linked to the correct

registration of image pixels seem unsuitable. A study that is related to the template

database security (type 6 attack described in section 3.1) is given in [HIL, 2001].

Using a commercial fingerprint matcher, the minutiae template data is reverse

engineered by the author and the corresponding synthetic fingerprint images are

generated. Although the generated images are not very realistic and few

experimental results are provided, the possibility of this masquerading may imply

that raw biometric templates need to be secured by using techniques say encryption.

Another method to protect templates from fraudulent usage involves using a

distorted (but noninvertible) version of the biometric signal or the feature vector. If a

specific representation of template is compromised, the distortion transform can be

replaced with another one from a transform database. Every application can use a

different transform (e.g., health care, visa, e-commerce) so that the privacy concerns

of subjects related to database sharing between institutions can be addressed.

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An attack system was designed for a minutiae-based fingerprint authentication

system [ULU, 2004A]. Based on this system, the author of thesis has proposed a new

scheme for [KAN, 2009B] cancelable biometrics. In the paragraph below, an

overview of the attack system designed by [ULU, 2004A] is given.

Let D and T be the representation of the Database Template and Synthetic Template

respectively. Each minutia may be described by a number of attributes, including its

location in the fingerprint image, orientation, type etc. Most common minutiae

matching algorithms consider each minutiae as a triplet m={x,y,�} that indicates the

minutiae location coordinates and the minutiae angle �.

D= {m1,m2,…….mn} mi = {xi,yi,�i} i= 1….m

T= { m’1,m’2,…….m’n } mj = {x’j,y’j,�’j} j= 1….n

Where m and n denotes the number of minutiae in D and T respectively.

• Di : The database template corresponding to user i , i =1, 2,3,....N , where N is

the total number of users registered in the system. It is assumed that the attacking

system knows the format of this template, but it cannot access the template itself.

• Tij : The jth synthetic template generated by the attacking system for user i. This

template has the same format as database templates; it can be represented as S

(Di, Tij): The matching score between Di and Tij�.

• Sthreshold : The decision threshold used by the matcher. Two templates will be

considered as matched if their matching score meet this value.

Algorithm 1.

For attacking a specific user account, the attacking system must follow the following

five steps: [ULU, 2004] also shown in Figure 3.7.

� Step 1 (Initial guessing): Generate a fixed number of synthetic templates (Ti1,

Ti2, Ti3 ……… Ti100).

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Figure 3.7 Overview of Attack System.

� Step 2 (Try initial guesses): accumulate the corresponding matching scores

[S(Di ,Ti1), S(Di ,Ti2 ), S(Di ,Ti3 ),..., S(Di ,Ti100)] for user i.

� Step 3 (Pick the best initial guess): Declare the best guess Tibest to be the template

resulting in the highest matching score.

� Step 4: Modify Tibest by adding a new minutia, replacing an existing minutia. If

for any one of these attempts, the matching score is larger than previous Sbest(Di)

declare the modified template as Tibest , and update Sbest(Di) accordingly.

� Step 5 (Obtaining result): If the current best score is accepted by the matcher

(namely, Sbest(Di) ��SThreshold ), stop the attack.

3.4.3 Proposed Work: Protection of Biometric Template The proposed algorithm of attack works on the basis of cancelable technique. Here

we will not store the templates in database in original; rather these will be stored

after applying some hashing. The above discussed algorithm of attack will be

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successful if we store our template Di in database without any change. But, if we

apply cancelable biometrics and store our template Di in database such that all Di in

database are not in original form, rather they are mutants only. Such that if D is

database templates as follows:

D= {m1,m2,…….mn} mi = {xi,yi,�i} i= 1….m

Then their mutants D’ = H(D) will be stored in database instead of actual D.

D’= {m’1,m’2,…….m’n} mi = {Xi,Yi,�i} i= 1….m

Where X = H(x) Y= H (y) and � = H (�)

Figure3.8 Blocks in the original image are subsequently scrambled randomly.

H is hashing function corresponds to any transformation applied to actual Templates

as shown in figure3.7 above. It is also true that there will be no math between actual

template and its mutant (those are actually stored). If we calculate Spatial Distance

(Sd) and direction difference (Dd) that will not be below r0 and �0 or we can write as

Sd(m’1, m1)= sqrt [(Xi - xi )2 + (Yi - yi )2 ] < r0 ----------------(1)

Similarly

Dd(m’1, m1) < �0 ----------------(2)

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Now let’s apply the Algorithm-1 to find the best match between existing templates.

Keeping in mind that now instead of D, D’ are stored in database. Suppose the

algorithm declares the D’i as best match due to its score level Sbest(D’i).

Since Spatial Distance (Sd) and direction difference (Dd) of D and D’ does not

match (From equation 1 & 2 above). No doubt the synthetic templates T and

Database Templates D will not match and similarly from equations (1) and (2).

Spatial Distance = Sd(Tm’1, Dm1) = sqrt [(Xi – x’i )2 + (Yi – y’i )2 ] < r0

and

Direction difference = Dd(Tm’1, Dm1) < �0

With this approach, only mutants will be stolen and original templates are quite safe.

We can further alter D to D” by some another hashing function (say W) in future

whenever required. In this way the cancelable biometrics helps a lot in safekeeping

our templates in database. Cancellation simply requires the specification of a new

distortion transform. Privacy is enhanced because different distortions can be used

for different services and the true biometrics are always safe. In addition, such

distorted biometrics cannot be used for searching legacy databases and will thus ease

some privacy violation concerns. A single template protection approach may not be

sufficient to meet all the application requirements. Hence, hybrid schemes that make

use of the advantages of the different template protection approaches must be

developed.

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3.5 Summary

In this chapter the author has proposed three different techniques to improve the

security of the biometric systems based on cryptography, steganography and

cancellable biometrics. In the traditional cryptography the secret key is used to

generate cipher text and at the receiving end the same secret key is used to decode

the message. The main problems associated with the traditional cryptographic

systems are that of key entropy, key uniqueness and key stability. In the proposed

approach, the biometric (secret) template replaces the secret key to generate the

cipher text and the same biometric template is used to decode the message at the

receiving end. It has eliminated the need to remember complicated key sequences

that could be forgotten, stolen or even guessed and thereby making the system more

secure.

Steganography can be thought of as higher version of the cryptography and hence its

usage can further increase the integrity and security of the biometric systems. The

secret key in the proposed approach has been embedded in the biometric template by

changing the bit pattern information of individual pixel. An algorithm is proposed for

this purpose that makes the biometric system secure against spoofing as the secret

key is almost impossible to be judged by any impostor because it is hidden in the

template.

The third proposed approach to secure biometric system is based on the concept of

cancelable biometrics. One of the most critical problems associated with the

biometric template is that if somehow biometric data is compromised then it is

compromised forever. In other words, unlike to the passwords or tokens based

security systems, another set of security (in case secret template is hacked or stolen)

cannot be issued to a person in the biometric-based systems. The proposed approach

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has addressed this problem by using the cancelable biometric. Original biometric

template is distorted by using some hash function to produce new biometric template

that is used for authentication purpose. In case the secret biometric template is

hacked or stolen then a new biometric template can be generated by using some

another hash function. The original biometric template is always safe as it is never

stored anywhere and thereby increases the security of the system. Further, the

proposed approach can enhance the template security by generating a number of

templates from the single template and using them for different services.

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Chapter 4

Making Biometric Systems More Efficient

4.1 Introduction

Fingerprints are the most widely used biometric feature for person identification and

verification in the field of biometric identification. Fingerprints possess two main

types of features that are used for automatic fingerprint identification and

verification: (i) Ridge and Furrow structure that forms a special pattern in the central

region of the fingerprint and (ii) Minutiae details associated with the local ridge and

furrow structure. Fingerprints are, therefore, used in forensic divisions worldwide for

criminal investigations. More recently, an increasing number of civilian and

commercial applications are either using or actively considering using fingerprint

based identification because of a better understanding of fingerprints as well as

demonstrated matching performance than any other existing biometric technology.

Henry Fauld [HEN, 2003] first scientifically suggested the individuality and

uniqueness of fingerprints. This discovery established the foundation of modern

fingerprint identification. Francis Galton conducted an extensive study of

fingerprints [LEE, 1991] and introduced the minutiae features for single fingerprint

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classification. The discovery of uniqueness of fingerprints caused an immediate

decline in the prevalent use of anthropometric methods of identification and led to

the adoption of fingerprints as a more efficient method of identification. In a

traditional biometric recognition system, the biometric template is usually stored on

a central server during enrollment. The candidate biometric template captured by the

biometric device is sent to the server where the processing and matching steps are

performed.

The rest of the chapter has been divided into the following three sections and

presents the approaches, developed by the author of this thesis to enhance the

efficiencies of existing biometric system.

Section 4.2 presents an approach to reduce the processing time during fingerprint

matching process in a Biometric System. The proposed work is based upon four

major classifications of fingerprint, whorl, arch, left-loop and right-loop and is more

efficient as compared with the existing system.

Section 4.3 presents a simple and effective method to speed up the process of

fingerprint verification scheme based on thumb geometry system. The proposed

scheme works in two phases and allows the complete controlled and automated

fingerprint verification with efficient response time which is found approximately 5

times better as compared with existing system. The proposed system also reduces the

FAR (False Accept Rate).

Section 4.4 deals with the effective use of soft biometrics and demonstrates that the

utilization of ancillary user information like gender, height, and ethnicity can

improve the performance of the traditional biometric systems. Though the soft

biometric characteristics are not as permanent and reliable as the traditional

biometric identifiers like fingerprint, face, iris etc yet they provides useful

information about the identity of the user that leads to higher accuracy in establishing

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the user identity. The proposed work also shows that soft biometric characteristics

help when these are used with primary biometric traits.

4.2 Reducing Process-Time for Fingerprint Identification

System Fingerprint Identification is the method of user-identification by making use of the

fingerprint impressions. The method usually works on the basis of minutiae points

found on the fingerprint. No two persons have exactly the same arrangement of ridge

patterns, and the patterns of any one individual remain unchanged throughout life.

Fingerprints offer foolproof means of personal identification.

Fingerprint identification process can be carried out in two ways: minutiae-based and

correlation based. The commonly used approach with minimum FAR (false accept

rate) and minimum FRR (false reject rate) is minutiae based technique. In this

process, first minutiae points are calculated and then are mapped their relative

placement on the finger.

Fingerprint Identification System determines the matched template of input

fingerprint with a stored fingerprint template in Database. The Database of templates

is obtained by fingerprint enrollment process. The Identification process starts by

putting the user’s fingerprint at sensor level. The input fingerprint then processed in

various step like; acquisition, pre-processing, feature extraction and matching as

shown in Figure 4.1. In acquisition stage the biometric system get the input template

using sensor, preprocessing includes image enhancement, gray level adjustment,

ridge thinning, etc.

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Figure 4.1 Block diagram of Fingerprint Identification system.

After having the fingerprint image preprocessed, the feature extraction block extracts

the minutiae points from template and then the extracted minutiae point undergoes

with a matching process to match with the stored database Templates and finally

identify the person if it is in system database. The database of biometric system plays

very important role in identification process as all the templates are stored in it. The

system performance also depends on the architecture of system database that is the

way templates are stored there in. The system performance decreases, if templates

are stored randomly as compared with if templates are stored in a particular manner.

To store the templates in a proper way, Sir Henry has given a classification scheme.

The following section highlights the Henry classification scheme.

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4.2.1 Henry Classification Scheme The Henry classification scheme allows for logical categorization of fingerprint

records into Primary Group Ratio (PGR) based on fingerprint pattern types. To find

the PGR values, Henry classification scheme assigns each finger a number according

to the order in which it is located in the hand, beginning with the right thumb as

number 1 and ending with the left pinky/little as number 10 as shown in Table 4.1.

Table 4.1 Henry Classification Table

Henry also assigns a numerical value to fingers that contain a whorl pattern; fingers

1 and 2 each have a value of 16, fingers 3 and 4 have a value of 8, and so on, with

the last two fingers having a value of 1.

Figure 4.2 Palmprints of a single person.

R

Thumb

R

Index

R

Middle

R

Ring

R

Pinky

L

Thumb

L

Index

L

Middle

L

Ring

L

Pinky

Finger Number

1 2 3 4 5 6 7 8 9 10

Value (If Whorl)

16 16 8 8 4 4 2 2 1 1

Pattern Type

loop whorl arch arch Loop arch loop whorl loop arch

Finger Value

0 16 0 0 0 0 0 2 0 0

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Fingers with a non-whorl pattern, such as an arch or loop pattern, have a value of

zero. Both palm prints of a person (as shown in Figure 4.2) are required to apply the

Henry classification scheme.

According to the Henry classification scheme, fingerprints are stored in the database

with their relative PGR values. The fingerprint Primary Group Ratio is determined

by calculating the ratio of one plus the sum of the values of the whorl-patterned,

even-numbered fingers; divided by one plus the sum of the values of the whorl-

patterned, odd-numbered fingers (Harling 1996). Mathematically the formula can be

written as:

Primary Grouping Ratio (PGR) =

To illustrate the computation of PGR values, consider an individual having a

fingerprint record with a LWAALALWLA pattern series (where L stands for Loop,

W stands for Whorl and A stands for Arch). The series begins with Finger 1 (i.e.

right thumb) and ending with Finger 10 (i.e. left pinky finger). By referring to table

4.1 the weights of even finger having whorl pattern are 16 and 2 (which have been

shown as bold in Table 4.1). Further no odd finger has whorl pattern in the given

series, hence the sum of its value is nil.

PGR =

Therefore, this individual belongs to the 19:1 primary group. This PGR ratio is

always treated as 19/1 not 19, as PGR contains both numerator and denominator. In

1+ (Sum of EVEN, whorled, finger values)

1+ (Sum of ODD, whorled finger value)

1+ (Sum of EVEN, whorled, finger values)

1+ (Sum of ODD, whorled finger value)

1+ (16+2)

1+ (0) = =

19

1

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case an individual does not have any whorl-patterned fingerprints, his classification

ratio, or primary group, would be 1:1. On the other hand if an individual having all

ten fingerprints containing a whorl pattern, his classification ratio would be 31:31.

Henry classification scheme reduce the effort to search a large templates of database,

as fingerprints are now stored with their respective PGR values. However Henry

classification scheme has some limitations as given below:

(i) Henry classification scheme work, only when both palm-prints of person are

stored in database. The scheme could not work with single fingerprint template.

(ii) The problem can also arise if the criminal as made some trick while enrolling his

palm-print to system. For instance, he can change the normal order of his fingers on

the sensor.

(ii) Amount of computer memory increases to store both palms of person as

compared to single fingerprint template.

4.2.2 The Proposed Method to Speed-up Fingerprint Matching

Process The author of this thesis presents a method [KAN, 2009] based on the theory of

fingerprint classification to address the problem stated above. Unlike to the Henry

classification, the proposed approach can work with a single fingerprint. This single

print can be a thumb print or print of an index finger.

In a conventional system, the database contains the fingerprint templates in an

ordinary manner. But in the proposed system the database contains the different set

of templates according to their classification that is achieved during the enrollment

process. The schematic diagram of the proposed approach is shown in Figure 4.3 and

the enrollment/identification process of the approach is described below:

Enrollment Process

(i) Sensor Module, senses the Fingerprint Templates

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(ii) Feature Extraction Module, extracts the minutiae points from Fingerprint

Template.

(iii) Fingerprint Classifier Module, classifies the input templates based on

Left-Loop, Right-Loop, Whorl or Arch and store them into the database.

Figure 4.3 Proposed Classifier for Fingerprint Identification.

Identification Process

(i) Sensor Module senses the input Fingerprint Template.

(ii) Feature Extraction Module, extracts the minutiae points from Fingerprint

Template.

(iii) A minutiae template has been generated of a particular classification and

search the database for its specific domain. The particular domain is then

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extracted from database and matched with the Minutiae Template as

shown in Figure 4.3.

In the database classification, only whorl, arch and loops are considered because

loops make up nearly 65% of all fingerprints, whorls are nearly 30%, and perhaps

5% are arches [BEI, 2003]. In the proposed approach, loops are further divided into

two categories viz. left-loop and right-loop as loops contribute 65% of the total

fingerprints. Consequently, there become four domains i) Left-Loop ii) Right-Loop

iii) Arch and iv) Whorl as shown in figure 4.4 below.

Whorl Arch Right-Loop Left-Loop

Figures 4.4 Position of Deltas in different Fingerprints.

The proposed classifier classifies the input templates into four categories (mentioned

above) on the basis of singular points. Various singular points in any fingerprint are

as given in Figure 4.5. Many types of minutiae’s exist, including dots (very small

ridges), islands (ridges slightly longer than dots, ponds or lakes (empty spaces

between two temporarily divergent ridges), spurs (a notch protruding from a ridge),

bridges (small ridges joining two longer adjacent ridges), and crossovers (two ridges

which cross each other).

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Figure 4.5 Minutiae- points on a fingerprint.

The algorithm for the proposed classifiers is shown in Figure 4.6. If the value of ND

(i.e. number of deltas) is 2 then it is either taken as whorl or twin loop. If the value of

ND is 1 then it is further computed for Left-Loop or Right-Loop. If the value of ND is

0 then it is marked as Arch. To find the category of loop (right or left) the feature

extraction stage estimates an axis locally symmetric to the ridge structures at the core

and computes � (number of ridges crossing the line segment joining core and delta).

The relative position, R, of delta with respect to symmetry axis is determined as

follows R = 1 if the delta is on the right side of symmetry axis and it will be left loop

otherwise R = 0 and in this case it will be a right loop. In this way we find the

categories of input fingerprint and build our database containing different domains of

fingerprints. Now let’s find the efficiency of proposed approach.

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Figure 4.6 Working of proposed Fingerprint classifier

The proposed approach proves very efficient especially when the stored database is

very large that is having more than 1, 00000 templates.

The proposed method was evaluated at Madhuban Forensic Laboratory, Karnal,

where the system database contains more than 1, 50,000 templates. The software

used at Madhuban Forensic Laboratory is FACTS (Finger Analysis Criminal Tracing

System) developed by CMC, New Delhi based on Henry classification scheme as

discussed above.

The experiment was conducted on FACTS with an input template (my own

fingerprint). The system took 22.35 minutes to produce output. There were 34

templates in output. These outputs are further subjected to human verification and

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that took 4.55 hours to give the exact identification. Therefore it is obvious that the

existing classification scheme is very time consuming and involves complication of

human verification. Two templates are declared as matched if their Spatial distance

and Direction are below than specified values as given by L. Hong. The algorithm is

discussed below:

Let D and T be the representation of the Database Template and Target Template

respectively. Each minutia may be described by a number of attributes, including its

location in the fingerprint image, orientation, type etc. Most common minutiae

matching algorithms consider each minutiae as a triplet m= {x,y,�}that indicates the

minutiae location coordinates and the minutiae angle �.

D= {m1,m2,…….mn} mi = {xi,yi,�i} i= 1….m

T= { m’1,m’2,…….m’n } mj = {x’j,y’j,�’j} j= 1….n

Where m and n denotes the number of minutiae in D and T respectively.

Database Template and Target Template and stored template will be matched, If we

calculate Spatial Distance (SD) and direction difference (DD) that will below than

specified value r0 and �0 or we can write as [HON, 1998A].

SD (m’1, m1) = sqrt [(x’i - xi )2 + (y’i - yi )2 ] <= r0

Similarly

DD (m’1, m1) <= �0

Where r0 and �0 are minimum threshold values.

Now the efficiency of proposed system is computed as follows:

Assume one match takes 1ms time and there are 1, 50,000 templates in the database.

Calculation for the best case:

The template is first match, time required = 1 X 1 = 1 ms.

Calculation for the worst case:

The database templates (1, 50,000 templates) are classified into the four domains

(Whorl domain, Left-Loop domain, Right-Loop domain and Arch domain) as

discussed in section 4.2.5 are computed below:

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Number of templates in Whorl-domain = 45000 templates.

(That is 30% of 1, 50,000).

Number of templates in Left-Loop-domain = 48000 templates.

(That is 32% of 1, 50,000).

Number of templates in Right-Loop-domain: 49500 templates.

(That is 33% of 1, 50,000).

Number of templates in Arch-domain: 7500 templates.

(That is 5% of 1, 50,000).

The input template may belong to either whorl domain or left-loop domain or right-

loop domain or arch domain. Now the amount of time required to match the input

template with the database template is computed below:

If input template is found in Whorl domain, It takes = 1ms X 45000= 45 sec.

If input template is found in Left-Loop domain, It takes = 1ms X 48000= 48 sec.

If input template is found in Whorl domain, It takes = 1ms X 49500= 49.5 sec

If input template is found in Whorl domain, It takes= 1msX 7500= 7.5 sec.

Average time for worst case = (45+48+49.5+7.5)/4= 150/4= 37.4 sec.

Efficiency of Existing System:

Calculation for the best case:

The template is First match, Time required = 1 X 1 = 1 ms

Calculation for the worst case:

The template is last match, Time required = 1 X 1, 50000 = 150 sec. = 25 min.

Time taken by proposed approach

Time taken by existing approach

37.4 sec.

25 min = =

1

40.1 Speedup Factor = =

116

This proves that the proposed approach is 40 times better than the existing Henry

classification scheme. The other obvious advantages of proposed approach are (i) it

considerably reduces the amount of memory required to store the fingerprint

template as only one print is stored instead of 10 prints of palms for an individual (ii)

The criminal can not fool the system as there is no order of fingerprints now. Still

there are some problems in existing system. In proposed system database contain

only single fingerprint instead of all the fingerprints of both palm, due to which the

system may not work if we try to match with input finger as middle finger or little

finger as they are not stored in database.

4.3 Performance Elevation of Fingerprint Verification System

Biometric systems have now been deployed in various commercial, civilian and

forensic applications as a means of establishing identity. These systems are based on

the evidence of fingerprints, hand geometry, iris, retina, face, vein-pattern, facial

thermo-gram, signature, voice, etc. to either validate or determine an identity [PRA,

2003]. The template feature set is typically generated during enrollment when a user

first interacts with the system and is refreshed or updated over a period of time in

order to account for intra-class variations [MAL, 2003]. Ideally, the feature set

extracted from the raw data is expected to be an invariant representation of a

person’s biometric. A generic biometric system has four important modules (a)

sensor module - it acquires the raw biometric data of an individual (b) feature

extraction module - it processes the acquired data to extract a feature set that

represents the biometric trait (c) matching module - it compares the extracted feature

set against the templates residing in the database through the generation of matching

117

scores (d) decision-making module - in which the matching scores are used to either

validate the user’s claimed identity (verification) or determine his identity

(identification).

This section discusses an approach developed by the author of this thesis to improve

the existing conventional fingerprint verification system. In the proposed method,

dimensions of Finger/Thumb are matched in first phase and the minute points are

calculated and matched in next phase only when the dimensions are matched in first

phase. The author of this thesis has proved that the proposed approach improves

FAR (False Accept Rate) and Total Response Time of Biometric System as

compared with Conventional Fingerprint Verification Systems. The approach is

useful when fingerprint verifications are made at large scale level.

4.3.1 Critical Factors Identified Affecting the Performance of

Biometric System In conventional fingerprint verification system, minute points are extracted and

compare with the existing template in the database. The Feature extractor module

finds the ridge endings and ridge bifurcations from the input fingerprint images. If

ridges can be perfectly located in an input fingerprint image, then minutiae extraction

is just a trivial task of extracting singular points in a thinned ridge map [ROS, 2004].

However, in practice, it is not always possible to obtain a perfect ridge map. The

performance of currently available minutiae extraction algorithms depends heavily

on the quality of the input fingerprint images [PRA, 2002]. Due to a number of

factors (aberrant formations of epidermal ridges of fingerprints, postnatal marks,

occupational marks, problems with acquisition devices, etc.); fingerprint images may

not always have well-defined ridge structures. A reliable minutiae extraction

algorithm is critical to the performance of an automatic identity authentication

system using fingerprints. The flowchart of a typical feature extraction algorithm is

depicted in Figure 4.7 below. It mainly consists of five components [MAI, 2002] viz.

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(i) Input fingerprint image (ii) Finding Orientation field (iii) Extract effective ridges

(iv) Thinned the Extracted ridges (v) Extract minutiae points. The algorithm shown

in Figure 4.7 is inefficient as it involves five stages that leads to increase in

complexity and response time.

To address the above said problem, the author of this thesis [KAN, 2007A] presents

an approach by combining the hand geometry verification system with fingerprint

verification system. Hand geometry system has faster response time compared to any

other system due to its minimum requirement of memory for template data storage as

discussed in the next section.

Figure 4.7 Steps for Extraction of minutiae points from input fingerprint

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4.3.2 Memory Requirements for Biometric Templates Table 4.2 shows the memory requirements (in bytes) for different types of biometric

traits. It is obvious that if the size of memory for template is large, it will increase the

size of database templates. It increases the system complexity and thereby response

time of system.

Table 4.2 Data Size for Biometric Templates Biometrics Traits Memory Required

(bytes) Retinal Scan 35

Iris Scan 256

Fingerprints 512 - 1000

Hand Geometry 9

Table 4.2 is adopted from [RUG, 1996] shows that the memory requirement for hand

geometry template is 9 bytes whereas for other templates it varies between 35 to

1000 bytes.

4.3.3 The Proposed Approach to Improve Response Time of

Biometric System

Figure 4.9 shows the architecture of the proposed approach, which consists of two

phase’s viz. Phase-I and Phase-II. Phase-I takes the input from sensor and then

measures the dimensions of the input thumb. The thumb dimension measurements; a,

b, c, d as shown in Figure 4.8 is computed. After extracting these parameters, it is

matched with database templates for comparison. If the match is successful, only

then it goes to phase-II for minutiae extraction else match is unsuccessful and no

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further processing is required. Phase-II extracts the minutiae points for input

fingerprint template by using the algorithm given in Figure 4.7.

(a) Side-View (b) Top-View

Figure 4.8 Measuring Thumb Dimensions during Phase-I

After having extracted these minutiae, it performs the required amount of translation

or rotation to fit the extracted minutiae points with database minutiae points. Then it

compares the minutiae points for both templates with the help of matching module in

phase-II. If match is successful, it is declared as ‘Verified’ and then ‘Stop’ otherwise

it is declared as ‘Not-Verified’ and then ‘Stop’.

The proposed approach is more efficient than other existing approaches on two

accounts; (i) if input thumb geometry is not matched during Phase-I then the

proposed algorithm discards the further processing of Phase-II and saves time. (ii) in

multimodal approach [HON, 1998], two or more biometric traits are employed,

which further increases size, cost and complexity of the system. While the proposed

approach is applied on single biometric trait.

121

Figure 4.9 Architecture of Proposed Approach

122

4.3.4 Performance Estimation for the Proposed Approach To estimate the performance of proposed approach, let’s assume that there are

10,000 input templates which are to be compared against database templates. It is

further assumed that out of 10,000 templates only 100 templates got verified. Let

time taken by geometry verification system (i.e. Phase-I) is t and time taken by

fingerprint verification system (i.e. Phase-II) is T and (Where T is five times greater

than t given by [RUG, 1996]).

Calculation of Response Time:

Response Time (Tc) for conventional Fingerprint Verification System

Tc = 10,000 X T unit time.

Tc = 10,000 X 5t unit time.

(Because in conventional verification system, Phase-II is involved)

Response Time for Proposed System

Tp = 9900*t + 100 *(T + t).

(As per assumption only 100 templates are matched and rest of 9900 templates

are discard at Phase-I.)

Tp = 9900*t + 100(5t+t) = 9900t+600t=10500t unit time.

Hence from the above calculation; it is obvious that the proposed approach is

approximately five times better than the existing biometric system. The proposed

approach allows completely controlled and automated fingerprint verification with

efficient response time and minimum FAR (False Accept Rate) as fingerprint is now

Tp

Tc

10,500t

50,000t =

1

4.7 Speedup Factor = =

123

checked at two phases. The proposed approach checks the dimensions as well as the

minutiae points of input template, after that it allows the user to be ‘Verified’.

4.4 Performance Improvement by using Soft

Biometric Traits

The fundamental requirement of any biometric recognition system is a human trait

having several desirable properties like universality, distinctiveness, permanence,

acceptability. However, the human trait that possesses all these properties in an ideal

way has not yet been identified. As a result, none of the existing biometric systems

provide perfect recognition and there is a scope for improving the performance of

these systems.

The human characteristic like gender, height, weight and age can also be used for

identification purpose. Although these characteristic are not unique and reliable, yet

they provide useful information about the user. These characteristics known as soft-

biometric traits and can be integrated with the primary biometric identifiers like

fingerprint, face, iris, signature for identification purposes.

The author of this thesis propose the utilization of “soft” biometric traits like gender,

height, weight, age, and ethnicity as an asset in the identification mechanism

provided by the Primary Biometric identifiers. Although soft biometric lacks the

distinctiveness and permanence to identify an individual uniquely and reliably yet

they provide some evidence about the user identity that could be beneficial.

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Unimodal biometric systems make use of a single biometric trait for user

recognition. It is difficult to achieve very high recognition rates using unimodal

systems due to problems like noisy sensor data and non-universality or lack of

distinctiveness of the chosen biometric trait. Multimodal biometric systems address

some of these problems by combining evidence obtained from multiple sources

[HON, 1999]. A multimodal biometric system that utilizes a number of different

biometric identifiers like face, fingerprint, hand-geometry, and iris can be more

robust to noise and minimize the problem of non-universality and lack of

distinctiveness.

However, the problem with multimodal system is that it will require a longer

verification time thereby causing inconvenience to the users. A multimodal biometric

system based on different biometric identifiers can be expected to be more robust to

noise, improve the matching accuracy and provide reasonable protection against

spoof attacks. But there are limitations as well.

(i) The overall cost involved in building the multimodal system can be high due to

the need for multiple high quality sensors and increased storage and computational

requirements.

(ii) The system will require a longer verification time thereby causing inconvenience

to the users.

A possible solution to the problem of designing a reliable and user-friendly biometric

system is to use additional information about the user like height, weight, age,

gender, ethnicity, and eye color to improve the performance of the primary biometric

system. Most of the biometric systems collect such additional information about the

users during enrollment and store them in the database as metadata. Biometric

systems used in access control applications generally have a human supervisor who

oversees the operations of the system. When a genuine user is falsely rejected by the

system, the human operator has to verify the identity of this user manually. This

125

manual verification is usually done by comparing the facial appearance of the user

with the facial image appearing on the user’s identification card and by verifying

other information on the ID card like age, gender, height, and other visible

identification marks. If the soft biometric characteristics can be automatically

extracted and used during the decision making process, the overall performance of

the system can be improved and the need of manual involvement will be reduced.

In order to utilize soft biometrics, there must be a mechanism to automatically

extract these features from the user during the recognition phase. As the user

interacts with the primary biometric system, the system should be able to

automatically measure the soft biometric characteristics like height, age and gender

without any interaction with the user. This can be achieved using a special system of

sensors. For example, a bundle of infra-red beams could be used to measure the

height. A camera could be used for obtaining the facial image of the user, by which

information like age, gender, and ethnicity could be derived [JAI, 1997]. These

observed soft biometrics information could then be used to supplement the identity

information provided by the user’s primary biometric identifier. Extensive studies

have been made to identify the gender, ethnicity, and pose of the users from their

facial images. Gutta et al. [GUT, 2000] proposed a mixture of experts consisting of

ensembles of radial basis functions for the classification of gender, ethnic origin, and

pose of human faces. Their gender classifier classified users as either male or female

with an average accuracy rate of 96%. Age determination is a more difficult problem

due to the very limited physiological or behavioral changes in the human body as the

person grows from one age group to another. There are currently no reliable

biometric indicators for age determination.

Heckathorn et al. [HEC, 1997] have shown that a combination of personal attributes

like age, gender, eye color, height, and other visible identification marks can be used

to identify an individual only with a limited accuracy. Hence, a system that is

126

completely based on soft biometric traits cannot meet the accuracy requirements of

real world applications. However, soft biometric traits can be used to improve the

performance of traditional biometric systems.

Primary Biometric System Soft Biometric System

Figure 4.10 Existing Soft Biometric System for person recognition.

Wayman [WAY, 1997] makes use of soft biometric traits like gender and age, for

filtering a large biometric database (as shown in Figure 4.10). Filtering refers to

limiting the number of entries in a database to be searched, based on characteristics

of the interacting user. For example, if the user can somehow be identified as a

middle-aged male, the search can be restricted only to the subjects with this profile

enrolled in the database. This greatly improves the speed or the search efficiency of

the biometric system. Filtering and system parameters tuning require an accurate

classification of a user into a particular class (e.g., male or female, blue or brown

eyes, Caucasian or Asian or African). This requires a pre-identification module that

can accurately perform this classification. The probability of user �i, given by

primary biometric feature vector x and the soft biometric feature vector y i.e., P(�i|

x; y) can be calculated using the Bayes’ rule.

127

P(�i|x; y) = P(y|�i) P(�i|x) ----------------- (i) P(y|�i)

Where P(�i| x) is the probability that the test user is �i given the feature vector x and

P(�i| y) is the probability that the test user is �i given the feature vector y.

The logarithm of P(�i|x; y) in equation (1) can be expressed as

log P(�i|x, y) = log P(y1|�i) + ……. + log P(yk|�i) + log P(yk+1|�i) + …… +log

P(ym|�i) + log P(�i|x) – log P(y)

Where m is the number of soft traits used in the system. This formulation has two

main drawbacks. The first problem is that all the m soft biometric variables have

been weighed equally. But in practice the gender of a person may give more

information about a person than height. Therefore, we must introduce a weighting

scheme for the soft biometric traits. Another problem is that any impostor can easily

spoof the system because the soft characteristics have an equal say in the decision as

the primary biometric trait. It is relatively easy to modify/hide one’s soft biometric

attributes by applying cosmetics and wearing other accessories (like mask, shoes

with high heels, etc.).

4.4.1 The Proposed Scheme to Integrate Soft Biometrics with

Primary Biometrics The author of this thesis has proposed a scheme [KAN, 2008A] to address to

problems stated in previous sub-section. In the proposed work the Biometric system

is divided into two subsystems. One subsystem is called the primary biometric

system and it is based on traditional biometric identifiers like fingerprint, face, hand-

geometry etc. The second subsystem, referred to as the secondary biometric system,

128

is based on soft biometric traits like age, gender, height etc. Architecture of personal

identification using primary biometrics and soft biometric is shown in Figure 4.11.

The proposed scheme consists of two stages viz. Stage-I and Stage-II. The soft

biometrics system is used as first stage and the primary biometric system is used as

second stage. The output of first stage is fed to second stage. This type of

combination will reduce the processing time system and will improve the efficiency

of system as explained in coming sub-section 4.4.7.

Processing at Stage-II is required only when the stage-I passes successfully else there

will be rejection automatically even without computing the stage-II. Stage-I also

computes the soft traits like age, gender, height automatically, without human

involvement.

Figure 4.11 Architecture of Personal Identification using Primary Biometrics and

Soft Biometric.

129

4.4.2 Performance Estimation of Proposed Scheme In the proposed work, the fingerprint is used as the primary biometric identifier and

age, gender and height are used as soft biometric traits. The soft traits are chosen in

such a way that they could be easily extracted automatically when user interact with

system. For estimating the performance of proposed scheme, let us assume there are

1,00,000 input templates to be matched against database templates and out of

1,00,000 templates only 100 templates are verified by the system. Let time taken by

primary biometric system to process single template is TF and time taken by soft

biometric to process single template is TS. Where TF >TS as soft traits can be

processed easily and TF is 10 times greater than TS [JAI, 2004A].

Calculation for the Response Time:

In conventional Fingerprint verification system Total Processing Time (TR) for

1,00,000 templates.

TR = 100000*TF unit time.

TR = 100000*10TS =1000000TS unit time

In Proposed system Total Processing Time (TP) for 1,00,000) templates.

Tp = (100000-100)* TS + 100 *(TF + TS).

(As per assumption only 100 templates are verified and rest of 99900 templates are

discard at Stage-I.)

Tp = 99900 TS + 100(10+1) TS = (99900+1100) TS =101000 TS unit time.

TP

TR

10,1000 TS

10,00000 TS =

1

9.9 Speedup Factor = =

130

It is obvious from the above calculation that the proposed scheme is approximately

ten times better than the existing biometric system. The system proves to be more

efficient when huge number templates are taken at input side.

4.4.3 Summary In this chapter the author has proposed three different techniques to improve the

efficiency of the biometric systems. The Henry finger print classification scheme,

which classifies the fingerprints in the database according to their relative Primary

Grouping Ratio (PGR) values has several limitations such as (i) it works only when

both palm-prints of person are available; (ii) it can not work when intruder has made

some trick while enrolling his palm-print to system, for instance, he can change the

normal order of his fingers on the sensor; (iii) a huge amount of computer memory is

required to store fingerprints of both hands. A new technique has been proposed to

address these problems.

Loops contribute 65% of the total fingerprint and they are further classified into left

loop and right loop. The remaining 35% of the total fingerprint is contributed by

whorl and arch. The proposed classifier as discussed in section 4.2.2, classifies the

input template into the four domains on the basis of left-loop, right-loop, whorl or

arch during the enrolment process in order to create the template database, which is

vertically classified into these four domains. During identification process, a

minutiae template is generated of a particular classification domain and that template

is searched in the corresponding domain of the template database thereby reducing

the response time of the biometric system. Theoretically, it has been shown that the

proposed approach is approximately forty times more efficient as compared with the

existing approaches.

131

The second approach based on hand geometry improves the performance of the

fingerprint verification system. The proposed algorithm works in two phases viz.

Phase-I and Phase-II as discussed in section 4.3.3. The algorithm proceeds to Phase-

II only when Phase-I is successful thereby reducing the processing time for rejecting

the input template. The proposed approach has been found more efficient than other

existing approaches on two accounts (i) on theoretical basis it has been shown that

the proposed approach has reduced the response time five times approximately as

compared to the existing approach; (ii) intuitively the proposed approach requires

lesser storage space as compared to multimodal based approaches.

The third approach is based on soft biometrics such as gender, height, ethnicity etc.

Though the soft biometric characteristics are not as permanent and reliable as the

traditional biometric identifiers like fingerprint, yet they can provide some

information about the identity of the user that leads to higher accuracy in establishing

the user identity. The proposed algorithm integrates soft biometrics with the primary

biometrics and works in two stages as discussed. Processing of stage II is required

only when stage I is passed successfully thereby reducing the processing time. On

theoretical basis, it has been shown that the proposed algorithm is ten times

approximately more efficient than the existing algorithm.

132

Chapter 5

Conclusion and Future Scope From system perspective, both security and privacy are open problems with no

satisfactory solutions on the horizon. In this thesis work, fundamental roadblocks for

widespread adoption of biometrics as effective and efficient means for ensuring

system integrity and system security have been explored and some solutions to these

roadblocks have been suggested. An extensive survey of the existing work in the

domain of fingerprint based biometric systems has been carried out and the

algorithms for fingerprint feature extraction, enhancement, matching, and

classification are critically analyzed. It was found that the loop holes still existed in

the biometric systems and an intelligent imposter could easily spoof these systems.

In this thesis work, three techniques to improve security and three techniques to

increase efficiency of the biometric systems have been proposed.

In the traditional cryptography the secret key is used to generate cipher text and at

the receiving end the same secret key is used to decode the message. The main

problems found in the traditional cryptographic systems are that of key entropy, key

uniqueness and key stability as discussed in section 3.2.1. In the proposed

cryptographic based approach, the biometric (secret) template replaces the secret key

to generate the cipher text and the same biometric template is used to decode the

133

message at the receiving end. Thus the proposed approach has eliminated the need to

remember complicated key sequences that could be forgotten, stolen or even guessed

thereby making the biometric system more secure.

Another proposed approach to secure the biometric system is based on

steganography. Steganography can be thought of as higher version of the

cryptography and hence its usage can further increase the integrity and security of

the biometric systems. The secret key in the proposed approach has been embedded

in the biometric template by changing the bit pattern information of individual pixel

as discussed in section 3.3.5. The algorithm has been developed for this purpose and

any security system based on this algorithm cannot be spoofed easily. The hacker

can not be able to break the system even he know both the secret templates and

secret key because he don’t know, how the key and templates are mixed with binding

algorithm.

The third proposed approach to secure biometric system is based on the concept of

cancelable biometrics. One of the most critical problems associated with the

biometric template is that if somehow biometric data is compromised then it is

compromised forever. In other words, unlike to the passwords or tokens based

security systems, another set of security (in case secret template is hacked or stolen)

cannot be issued to a person in the biometric-based systems. The proposed approach

has addressed this problem by using the cancelable biometric. Original biometric

template is distorted by using some hash function to produce new biometric template

that is used for authentication purpose. In case the secret biometric template is

hacked or stolen then a new biometric template can be generated by using some

another hash function. The original biometric template is always safe as it is never

stored anywhere and thereby increases the security of the system. Further, the

proposed approach can enhance the template security by generating a number of

templates from the single template and using them for different services.

134

The Henry finger print classification scheme [HEN, 2003], which classifies the

fingerprints in the database according to their relative Primary Grouping Ratio

(PGR) values as discussed in section 4.2.1. It has several limitations such as i) it

works only when both palm-prints of person are available ii) it can not work when

intruder has made some trick while enrolling his palm-print to system, for instance,

he can change the normal order of his fingers on the sensor; c) a huge amount of

computer memory is required to store fingerprints of both hands. A new technique

has been proposed to address these problems.

Loops contribute 65% of the total fingerprint and they are further classified into left

loop and right loop. The remaining 35% of the total fingerprint is contributed by

whorl and arch. The proposed classifier as discussed in section 4.2.2, classifies the

input template into the four domains on the basis of left-loop, right-loop, whorl or

arch during the enrolment process in order to create the template database, which is

vertically classified into these four domains. During identification process, a

minutiae template is generated of a particular classification domain and that template

is searched in the corresponding domain of the template database thereby reducing

the response time of the biometric system. Theoretically, it has been shown that the

proposed approach is approximately forty times more efficient as compared with the

existing approaches.

The author of the thesis has proposed another approach based on hand geometry to

improve the performance of the fingerprint verification system. The proposed

algorithm works in two phases viz. Phase I and Phase II as discussed in section 4.3.3.

The algorithm proceeds to Phase-II only when Phase-I is successful thereby reducing

the processing time for rejecting the input template. The proposed approach has been

135

found more efficient than other existing approaches on two accounts (i) on

theoretical basis it has been shown that the proposed approach has reduced the

response time five times approximately as compared to the existing approach; (ii)

intuitively the proposed approach requires lesser storage space as compared to

multimodel based approaches.

The third approach, which has been proposed to reduce the response time, is based

on soft biometrics. The proposed algorithm integrates soft biometrics with the

primary biometrics and works in two stages as discussed in section 4.4.1. Processing

of stage-II is required only when stage-I is passed successfully thereby reducing the

processing time. On theoretical basis, it has been shown that the proposed algorithm

is ten times approximately more efficient than the existing algorithm.

As biometric technology matures, there will be an increasing interaction among the

market, technology, and the applications. This interaction will be influenced by the

added value of the technology, user acceptance, and the credibility of the service

provider. As biometrics continues to advance scientifically and technologically, its

use and acceptability as a means of security and authorization across various sectors

will also grow. Biometrics would be a useful solution to the issue of security for

mobile banking in rural areas as only thumb impression is quite enough for money

transaction. Many biometric technology providers are already delivering biometric

authentication for a variety of web-based and client/server based applications.

Continued improvements in the technology will increase performance at a lower

cost.

Though biometric authentication is not a magical solution that solves all

authentication concerns and also it does not guarantee for 100% accuracy and

security yet it make easier and cheaper for us to use a variety of automated

136

information systems. It is too early to predict where and how biometric technology

would evolve and get embedded in which applications. But it is certain that

biometrics based identification will have a profound influence on the way we

conduct our daily business. It is also certain that, the fingerprints will remain an

integral part of the preferred biometric-based identification solutions as the most

mature and well understood biometric in the future generation.

Further a single template protection approach may not be sufficient to meet all the

application requirements. Hence, hybrid schemes that make use of the advantages of

the different template protection approaches must be developed.

137

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