Multimodal fusion of fingerprint and iris

Prepared by Bhavesh H.Pandya

Guided by: Dr. Vinayak Bharadi

Registration No: Thakur/86

Multimodal Fusion of Fingerprint and

Iris using Hybrid Wavelet based

Feature vector

21-Jan-151 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

Flow of Presentation

Introduction

Literature Survey

Related Theory

Problem Definition

Design Implementation

Result and Discussion

Conclusions

Future scope

References

Publication


Importance of Project.

Motivation.

21-Jan-153

Introduction

MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

Importance of Project

Fingerprint & Iris features are extracted using

multilevel decomposition of fingerprint image

using a new family of wavelet called kekre’s

wavelet and the iris features are extracted using

hybrid wavelet type 1, type -2. In this project KNN

classifier used for unimodal fingerprint recognition

and multi-instance iris recognition. Feature vector

of iris and fingerprint are combined using decision

fusion technique.


Motivation

21-Jan-155

Biometrics comprises methods for uniquely

recognizing humans based upon one or more intrinsic

physical or Behavioral traits.

In computer science, in particular, biometrics is used

as a form of identity access management and access

control.

It is also used to identify individuals in groups that are

under surveillance [1].

By using biometrics it is possible to establish an

identity based on who you are, rather than by what

you possess, such as an ID card, or what you

remember, such as a password.

In some applications, biometrics may be used to

supplement ID cards and passwords thereby

imparting an additional level of security. Such an


Literature Survey


Sr.N

o

Paper Title Description

1 An Introduction to

Biometrics

Recognition [3]

•In this paper [3], Biometric recognition or, simply,

biometrics refers to the automatic recognition of

individuals based on their physiological and behavioral

characteristics.

•The results demonstrated that biometrics refers to

automatic recognition of an individual based on her

behavioral and/or physiological characteristics.

•Biometrics-based systems also have some limitations

that may have adverse implications for the security of a

system.

2 Iris Recognition

Using Discrete

Cosine Transform

and Kekre’s Fast

Codebook

Generation

Algorithm [71]

•In this paper [71], an iris recognition system based on

vector quantization and its performance is compared

with the Discrete Cosine Transform (DCT).

•The proposed VQ based system does not need any

pre-processing and segmentation of the iris.

•For vector quantization author used Kekre’s Fast

Codebook Generation Algorithm (KFCG).


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Sr.No Paper Title Description

3 Fingerprint – Iris

Fusion based

Identification

System using a

Single Hamming

Distance Matcher

[70]

•In this paper [70] author proposed a framework for

multimodal biometric fusion based on utilization of a

single matcher implementation for both modalities.

•The proposed framework is designed to provide

improved performance over the unimodal systems.

4 Multimodal

Biometric

Identification for

Large User

Population Using

Fingerprint, Face

and Iris

Recognition[24]

•This paper [24] overviews and discusses the various

scenarios that are possible in multimodal biometric

systems using fingerprint, face and iris recognition, the

levels of fusion that are possible and the integration

strategies that can be adopted to fuse information and

improve overall system accuracy.

5 Multimodal

Biometrics: Need

for Future Security

Systems [72]

•In this paper [72] author explained different aspects of

biometric identification systems, their types, current

architectures, future architecture and efforts towards

the development of common framework for biometric

identification. MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

21-Jan-159

Sr.No Paper Title Description

6 Fingerprint

Recognition Using

Wavelet Features

[74]

•The wavelet features are extracted directly from the

gray-scale fingerprint image with no pre-processing

(i.e. image enhancement, directional filtering, ridge

segmentation, and ridge thinning and minutiae

extraction). The proposed method has been tested on

a small fingerprint database using the k-nearest

neighbour (k-NN) classifier.

7 An Iris Recognition

System Using

Phase-Based

Image Matching

[75]

•In this paper, author consider the problem of

designing a compact phase based iris recognition

algorithm especially suitable for hardware

implementation.

•The prototype system fully utilizes state-of-the-art

DSP (Digital Signal Processor) technology to achieve

real-time iris recognition capability within a compact

hardware module.


•Biometrics•Fingerprint Recognition System.•Iris Recognition System.•Multimodal Biometrics•Fusion Techniques•Iris Localization

Related Theory


Biometrics Biometrics is the science by which we measure

the physiological and behavioral characteristics of

a person.

History of Biometrics device.

Biometrics systems are becoming popular as a

measure to identify human being by measuring

one’s physiological or behavioral characteristics.

Biometrics identifies the person by what the

person is rather than what the person carries,

unlike the conventional authorization systems like

smart cards.

Unlike the possession-based and knowledge-

based personal identification schemes, the

biometrics identifiers cannot be misplaced,

21-Jan-15MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

Biometrics Characteristics

12

Characteristics Meaning

Universality Each person should have the characteristic.

Uniqueness Indicates how well the biometric separates individuals

from another.

Permanence Measures how well a biometric resists aging and other

variance over time.

Collectability Ease of acquisition for measurement.

Performance Accuracy, speed, and robustness of technology used.

Acceptability Degree of approval of a technology.

Circumventio

n

Ease of use of a substitute.


Types of Biometrics

Physiological

DNA

Ear

Face

Facial, hand, and hand vein

Fingerprint

Gait

Hand and finger geometry


Types of Biometrics(cntd)

Iris

Keystroke

Odor

Palm print

Retinal scan

Behavioral

Signature

Voice


Types of Biometrics(cntd)

Fig : 1 Examples of biometric characteristics: (a) DNA, (b) ear, (c) face, (d) facial

thermogram, (e) hand thermogram, (f) hand vein, (g) fingerprint, (h) gait, (i) hand

geometry, (j) iris, (k) palmprint, (l) retina, (m) signature, and (n) voice.


Fingerprint Recognition System.

Fingerprint Pre-processing :

The fingerprint must be pre-processed to remove the effect of noise, effect of dryness, wetness of the finger and difference in the applied pressure while scanning the fingerprint. The pre-processing is a multi-step process. The different steps in pre-processing are as follows [29], [30], [31], [38].

Smoothening Filter

Intensity Normalization

Orientation Field Estimation

Fingerprint Segmentation

Ridge Extraction

Thinning


Iris Recognition SystemGenerally, iris recognition system consists of four

major steps. They include :

Image acquisition from iris scanner

Iris image pre-processing

Feature extraction

Enrolment / recognition.

Pre-processing :

Iris Feature Extraction Methods:


Multimodal Biometrics

The most compelling reason to combine different

modalities is to improve the recognition rate &

reliability. This can be done when biometric

features of different biometrics are statistically

independent. There are other reasons to combine

two or more biometrics. One is that the different

biometric modalities might be more appropriate

for the different applications.

Combinations of Biometric Traits


Multimodal Biometric Systems

Multimodal biometric systems take input from

single or multiple sensors measuring two or more

different modalities of biometric characteristics.

For example, a system combining face and iris

characteristics for biometric recognition

Multi-algorithmic

Multi-instance

Multi-sensorial


Performance Metrics

The performance of a biometric system is measured by

different parameters or metrics. The following are used

as performance metrics for biometric systems [1], [2], [3],

[5]:

False Accept Rate or False Match Rate (FAR or FMR)

False Reject Rate or False Non-Match Rate (FRR or

FNMR)

Equal Error Rate, Performance Index and Cprrect

Classification Ratio (PI and CCR)

Other metrics which are related to the sensor devices are

Failure to Enroll Rate (FTE), Failure to Capture Rate (FTC)

& Template Capacity.

Generally physiological biometric traits are more

accurate than behavioral biometrics [1], [6].


Application of Biometrics

Physical Access

Virtual Access

E-commerce Applications

Covert Surveillance


Problem Definition


The topic for research is ‘Multimodal Fusion of Fingerprint and Iris’. It consists of multi instance iris based biometric systems combined with Fingerprint based system.

Main focus of research is to use hybrid wavelet transforms on enrolled image data to extract the feature vector from Iris & Fingerprint. The hybrid wavelet transforms are generated using Discrete Walsh transform (DWT) and Kekre Wavelets (KW). Iris localization and feature vector for iris and fingerprint extracted. Fusion of Iris & Fingerprint based feature is performed. The system will be benchmarked by evaluating FAR, FRR, TAR, TRR,EER and CCR.


• Technology Used.

• Model Development .

• Hybrid Wavelet based Feature Extraction [80].

• Fingerprint Feature Extraction & Matching.

• Snapshots.

Design Implementation and

Analysis


Hardware and Software

Requirement

Hardware Requirement

Processor: Dual Core processor

RAM: 2 GB DDR3 or more

Hard disk: 40 GB or more

Software Requirement

Operating System : Windows 7 or higher

Programming Language : C# .Net

Development Kit : Visual studio 2012


Model Development

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Fig: 4 Architecture of the proposed system.


Hybrid Wavelet based Feature

Extraction [80]

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Fig: 5 Hybrid Wavelet Type I Transform of an Image (a) Original Image &

Hybrid Wavelet Type I Transform Level 1 Components (b) Kekre Wavelets

Components of other Image


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Multiresolution Analysis using Hybrid Wavelet and Proposed Method.


Fingerprint Feature Extraction &

Matching.

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Table :1 Fingerprint Samples Taken from Same User and Corresponding ROI

User Fingerprint 1 Fingerprint 2 Fingerprint 3 Fingerprint 4


Snapshots

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Fig: 6 User enrollment for Iris.


Iris Localization Process


Iris normalization


Test image and Standard

Image


Iris feature vector using hybrid

wavelet type - I and type – II.


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Multiresolution analysis of iris ROI for feature vector extraction (image

0-180).MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

21-Jan-1536

Multi resolution analysis of iris ROI for feature vector extraction (image


21-Jan-1537

Multi resolution analysis of iris ROI for feature vector extraction (image


User enrollment for

Fingerprint.


Ten Fingerprint sample


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Project Demonstration


Result and Discussion


Performance Metrics

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Total Number Genuine Fingerprints Rejected as Imposter

Total Number of Genuine Matching Tests PerformedFRR

Total Number Genuine Fingerprints Accepted

Total Number of Genuine Matching Tests PerformedTAR

Total Number Imposter Fingerprints Accepted as Genuine

Total Number of Forgery Tests PerformedFAR

Total Number Imposter Fingerprints Rejected

Total Number of Forgery Tests PerformedTRR

PI=100-EER


Iris Recognition Results

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Fig :7 Performance Comparison of Kekre’s & Haar Wavelets for Iris

RecognitionMF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

Fingerprint Recognition Results

21-Jan-1544

Sr. Type of WaveletPI Accuracy (%) –

CCR

1Hybrid Wavelet Type

I

78.3475.23

2Hybrid Wavelet Type

II

79.3277.78

3Kekre’s Wavelets

[78]

90.0084.40

4 Haar Wavelets [78]88.00

81.15

Table : Summary of Fingerprint Matching Tests


Fingerprint & Iris Score based Fusion

21-Jan-1545

Fig : 8 Performance Comparison of Fingerprint & Iris Multimodal

Fusion.MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

Conclusion


Hybrid wavelets based texture feature extraction

techniques are implemented. Besides this fusion of

biometric traits is also performed.

Multimodal biometric systems are discussed here.

Fusion of iris and fingerprint is done.

In this report we are using combination of kekre’s

wavelet and walsh transform hence name given as

hybrid wavelet. Hybrid wavelets have been used

effectively in in this research for texture feature

extraction of fingerprints, & Iris.

Iris and fingerprint feature vector and extraction

implemented using hybrid wavelet type I & II. Left and

Right iris images are considered separately.


When we compare hybrid wavelet type I with

hybrid wavelet II then we found that accuracy of

hybrid wavelet II is more than hybrid wavelet I.

Unimodal fingerprint recognition system is fused with

a multi-instance iris recognition system. Decision

level as well as feature level fusion is implemented.

Kekre’s wavelets are having better texture

information extraction capability as compared to the

Hybrid Wavelets.

We have used simple Euclidian distance based K-NN

classifier with Five training samples per person. This

is an example of multi-algorithmic biometric fusion.

Performance of Hybrid Wavelet Type II is better and

they give higher PI & CCR.21-Jan-1548 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

Future Scope


Some of the further work directions for improvement in

the results and implementation of variants of proposed

systems are as follows.

In this project, we combined Walsh transform and

Kekre’s Transform but we can combine Haar transform

and DCT, Hartley or any other transform and analyze

their performance.

We have used KNN classifier in this project but we can

use better classifier like SVM, Neural network etc.

Hybrid Wavelets can be used for feature extraction of

other biometric traits like Palmprints, Finger-knuckle

print, Face etc.


References


[1] A. K. Jain, P. Flynn, A. A Ross, “Handbook of Biometrics”, Springer, USA, ISBN-13: 978-0-387-71040-2, pp.1-23,

2007

[2] http://en.wikipedia.org/wiki/Biometrics , accessed on 07.07.2010, 10:42AM

[3] A. K. Jain, A. Ross, S. Prabhakar,―”An introduction to biometric recognition, Circuits and Systems for Video

Technology”, IEEE Transactions on, Vol. 14, No. 1, pp. 4-20, 2004

[4] L. O'Gorman, ― “Comparing Passwords, Tokens, and Biometrics for User Authentication”, Proc. IEEE, Vol. 91, No. 12,

pp. 2019- 2040, Dec. 2003

[5] J.D. Woodward, Jr.Nicholas M. Orlans, P. T. Higgins, "Biometrics", McGraw-Hill/Osborne,ISBN-0-07-222227-1, DOI:

10.1036/0072230304, 2003

[6] S. Liu, M. Silverman, "A practical guide to biometric security technology", IT Professional, Vol. 3, No. 1., pp. 27-32,

Aug.2002

[7] http://www.tsl.uk.com/ProductMC70TriScan.htm , accessed on 25.07.2010, 11.50 AM

[8] S. Prabhakar, S. Pankanti, A. K. Jain, "Biometric recognition: security and privacy concerns", IEEE In Security & Privacy,

IEEE, Vol. 1, No. 2., pp. 33-42, 2003

[9] L. Zhang, L. Zhang, D. Zhang, and H. Zhu, "Online Finger Knuckle print Verification for Personal Authentication", Pattern

Recognition, Vol. 43, No. 7, pp. 2560-2571, 2010

[10] http://archives.cnn.com/2000/TECH/computing/07/24/iris.explainer/index.html, Accessed on 16.07.2010, 10.33 AM.

[11] A. Moenssens, ―Forensic-Evidence.com: “Alphonse Bertillon and Ear Prints”, 2001, available at http://www.forensic-

evidence.com/site/ID/ID_bertillion.html.

[12] http://www.hitachi.com/rd/research/sdl/themes_sec_01.html

[13] N. K. Ratha, J. H. Connell, and R. M. Bolle, "Enhancing security and privacy in biometrics-based authentication

systems," IBM systems Journal, vol. 40, pp. 614-634, 2001 21-Jan-1552 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

[14] Andrew Beng Jin Teoh, David Chek Ling Ngo, "Biophasor: Token Supplemented Cancellable Biometrics",

Control, Automation, Robotics and Vision, ICARCV ‘06,pp.1-5, Dec. 2006’.

[15] The Biometric Consortium (http://www.biometrics.org)

[16] International Biometric Industry Association (http://www.ibia.org)

[17] K. Kraniger,R. A. Mocny, "Testimony of Deputy Assistant Secretary for Policy Kathleen Kraninger, Screening

Coordination, and Director Robert A. Mocny, US-VISIT, National Protection and Programs Directorate, before the House

Appropriations Committee, Subcommittee on Homeland Security, "Biometric Identification", March 2009, US Department

of Homeland Security, retrieved 20 February 2010

[18] http://news.bbc.co.uk/2/hi/south_asia/8598159.htm

[19] http://uidai.gov.in/

[20] http://www.biometricgroup.com/reports/public/market_report.php

[21] A. Ross, A. K. Jain, "Multimodal Biometrics: An Overview", In Proceedings of 12th European Signal Processing

Conference (EUSIPCO), Vienna, Austria, pp. 1221-1224, Sept. 2004

[22] A. Ross and A. K. Jain, ―”Information fusion in Biometrics, Pattern Recognition Letters”, vol. 24, pp. 2115–2125, Sep.

2003.

[23] L. I. Kuncheva, C. J. Whitaker, C. A. Shipp, and R. P. W. Duin, ―”Is Independence Good for Combining Classifiers?”, in

Proceedings of International Conference on Pattern Recognition (ICPR), Vol. 2, (Barcelona, Spain), pp.168–171, 2000

[24] Teddy Ko, ―”Multimodal Biometric Identification for Large User Population Using Fingerprint, Face and Iris Recognition”,

Proceedings of the 34th Applied Imagery and Pattern Recognition Workshop (AIPR05), 2005.

[25] S. Prabhakar, A. K. Jain, ―”Decision level Fusion in Fingerprint Verification, Pattern Recognition”, vol. 35, no. 4, pp. 861–874,

2002

[26] ―Summary of NIST Standards for Biometric Accuracy, Tamper Resistance, and Interoperability, November 13, 2002

[27] J. Fierrez-Aguilar, J. Ortega-Garcia and J. Gonzalez-Rodriguez, "Fusion strategies in Biometric Multimodal Verification",

Multimedia and Expo, IEEE International Conference on, Vol. 3, pp. 5-8, 2003


21-Jan-1554

[28] L. Hong and A. Jain, "Integrating Faces and Fingerprints for Personal Identification", IEEE Transactions on

Pattern Analysis and Machine Intelligence, Vol. 20, No. 12, pp. 1295-1307, Dec. 1998

[29] D. Maltoni, D. Maio, A. K. Jain, S. Prabhakar, "Handbook of Fingerprint Recognition", Second Edition, Springer, ISBN:

978-1- 84882-253-5,pp. 205-223, 2009 275

[30] H. B. Kekre, T. K. Sarode, V. M. Rawool, ―”Fingerprint Identification using Discrete Sine Transform (DST)”, International

Conference on Advanced Computing & Communication Technology (ICACCT-2008), Asia Pacific Institute of Information

Technology, Panipat, India, Nov. 2008

[31] H. B. Kekre, T. K. Sarode, S. D. Thepade,―”DCT Applied to Column Mean and Row Mean Vectors of Image for

Fingerprint Identification”, International Conference on Computer Networks and Security (ICCNS08), Pune, India, 27-28,

Sep. 2008

[32] C. Wu, V. Govindaraju, "Singularity Preserving Fingerprint Image Adaptive Filtering", In Proceedings of IEEE

International Conference on Image Processing, pp. 313–316, 2006

[33] C. Klimanee,D. T. Nguyen, "On the Design of 2-D Gabor Filtering of Fingerprint Images", In Proc. First IEEE Consumer

Communications and Networking Conference, CCNC 2004, Las Vegas, USA, pp. 430 - 435, 2004

[34] T. Kamei, M. Mizoguchi, "Image Filter Design for Fingerprint Enhancement", In Proceedings IEEE International

Symposium on Computer Vision”, pp. 109 - 114, 1995

[35] F.Alonso,J. Fierrez, J. Ortega, "An Enhanced Gabor Filter-Based Segmentation Algorithm for Fingerprint Recognition

Systems", Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, pp.239-244,

2005

[36] A. K. Jain, S. Prabhakar, L. Hong, "A Multichannel Approach to Fingerprint Classification", IEEE Transactions On Pattern

Analysis and Machine Intelligence, Vol. 21, No. 4, pp. 348-359, April 1999

[37] S. Chikkerur, V. Govindaraju, ―”Fingerprint image enhancement using STFT analysis”, In International Workshop on

Pattern Recognition for Crime Prevention, Security and Surveillance, ICAPR 05, pp. 20–29, 2005

[38] Sherlock B.G., Monro D.M., Millard K., "Fingerprint Enhancement By Directional Fourier Filtering", IEEE Proceedings of

Vision, Image and Signal Processing, Vol. 141, No. 2, pp. 87-94, Aug 2002

[39] A. K. Jain, L. Hong, S. Pankanti, R. Bolle, ―”An Identity Authentication System Using Fingerprints”, Proc. IEEE, Vol. 85,

pp. 1365–1388, Sept. 1997

[40] Dario Maio and Davide Maltoni, ―”Direct gray-scale minutiae detection in fingerprints”, IEEE Trans. on Pattern Analysis

and Machine Intelligence, vol. 19, pp. 27–40, Jan. 1997 276MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

21-Jan-1555

[41] A. Bazen, S. Gerez, ―”Segmentation of Fingerprint Images, in Proc. Workshop on Circuits Systems and

Signal Processing”, ProRISC 2001, pp. 276–280, 2001

[42] B. Mehtre, ―”Fingerprint Image Analysis for Automatic Identification , Machine Vision and Applications”, Vol. 6,

pp. 124–139, 1993

[43] D. Simon-Zorita, J. Ortega-Garcia, J. Fierrez-Aguilar, J. Gonzalez-Rodriguez, ―”Image Quality and Position

Variability Assessment In Minutiae-Based Fingerprint Verification”, IEE Proceedings - Vis. Image Signal

Processing, Vol. 150, pp. 402– 408, Dec. 2003

[44] Lin Lin Shen, Alex Kot, WaiMun Koo, ―”Quality Measures of Fingerprint Images”, In Proceedings of 3rd Audio

and Video- Based Person Authentication, AVBPA 2001, pp. 266–271,2001

[45] H. B. Kekre, V. A. Bharadi, "Fingerprint & Palmprint Segmentation by Automatic Thresholding of Gabor

Magnitude" , 2nd International Conference on Emerging Trends in Engineering & Technology , ICETET 2009 ,

pp.235-241, Dec. 2009

[46] R. C. Gonzalez, R. Woods, "Digital Image Processing", Pearson Education, Prentice hall India, pp.743-746

[47] A. K.Jain , S. Prabhakar, L. Hong, S. Pankanti, "Filter bank Based Fingerprint Matching", IEEE Transactions on

Image Processing, Vol. 9, No. 5, pp.846 - 859, May 2000

[48] A. Cavusoglu, S. Gorgunoglu, "A Robust Correlation based Fingerprint Matching Algorithm for Verification",

Journal of Applied Science, Vol. 7, Asian Network for Scientific Information, ISSN : 1812-5654 , pp. 233-238, 2007

[49] F. Afsar , M. Arif, M. Hussain, "Fingerprint Identification and Verification System using Minutiae Matching" , In

Proceedings of National Conference on Emerging Technologies, Pakistan Institute of Engineering & Applied

Sciences, Islamabad,

Pakistan,2007

[50] C. V. Kameswara Rao and K. Balck, "Finding The Core Point In A Fingerprint", IEEE Transactions on Computers,

Vol. C-27, No. 1, Jan. 1978

[51] S. Chikkukerurr, N. Ratha, ―”Impact of Singular Point Detection on Fingerprint Matching Performance, Automatic

Identification Advanced Technologies”, 2005. Fourth IEEE Workshop on , pp. 207 - 212 , Oct. 2005 277

[52] H. B. Kekre, V. A. Bharadi, "Fingerprint Core Point Detection Algorithm Using Orientation Field Based Multiple

Features", International Journal on Computer Applications (IJCA), France, Vol. 1, No. 15, pp.98-103, Feb. 2010

[53] R. Bahuguna, ―”Fingerprint Verification using Hologram Matched Filtering”, In Proceedings of Biometric

Consortium, 8th Meeting, San Jose, California, June 1996


21-Jan-1556

[54] M. Eshera, K. S. Fu, ―”A Graph Distance Measure For Image Analysis”, IEEE Transactions on Systems,

Man, and Cybernetics, Vol. 14, No. 3.,pp. 398-408, 1984

[55] M. A. Eshera and K.S. Fu, ―”A Similarity Measure between Attributed Relational Graphs for Image Analysis ,In

Proceedings of 7th International Conference on Pattern Recognition”, pp. 75- 77, Dec. 1984

[56] S. Gold and A. Rangarajan, ―”A Graduated Assignment Algorithm For Graph Matching, IEEE Transactions on

Pattern Analysis and Machine Intelligence”, Vol. 18, No. 4, pp. 377–388, 1996

[57] A. K. Hrechak and J. A. McHugh, ―”Automated Fingerprint Recognition Using Structural Matching, Journal of

Pattern Recognition”, Vol. 23, No. 8, 1990

[58] D. K. Isenor, S. G. Zaky, ―”Fingerprint Identification Using Graph Matching, Journal of Pattern Recognition”, Vol.

19, No. 2, 1986

[59] A. K. Jain, L. Hong, and R. Bolle, ―”On-line fingerprint verification, IEEE Transactions on Pattern Analysis and

Machine Intelligence”, Vol. 19, No. 4, pp. 302–314, Apr. 1997

[60] A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, ―”An Identity- Authentication System Using Fingerprints, In

Proceedings of IEEE”, Vol. 85, No. 9, pp. 1365–1388, Sep. 1997

[61] H B Kekre, V A Bharadi, "Palmprint Recognition Using Kekre‘s Wavelet‘s Energy Entropy Based Feature Vector",

Proceedings of International Conference & Workshop on Emerging Trends in Technology 2011, TCET, Mumbai,

India, pp. 39-45, Feb. 2011

[62] H. B. Kekre, V. A. Bharadi, "Finger-Knuckle-Print Region of Interest Segmentation using Gradient Field Orientation

& Coherence ", Proceedings of IEEE International Conference ICETET 2010, India, pp. 43-50, Dec. 2010

[63] H. B. Kekre, T. K. Sarode, V. A. Bharadi, T. Bajaj, S. Chatterjee, M. Bhat, K. Bihani, "A Comparative Study of DCT

and Kekre‘s Median Code Book Generation Algorithm for Face Recognition", ICWET '10 Proceedings of the

International Conference and Workshop on Emerging Trends in Technology, pp. 343-348, Feb. 201

[64] H. B. Kekre, V. A. Bharadi, "Face Recognition using Kekre‘s Wavelets Energy & Performance Analysis of Feature

Vector Variants", Proceedings of ACM International Conference ICWET 2011, Mumbai, India, Vol. 1, pp.39-45,

Feb. 2011

[65] H. B. Kekre, T. K. Sarode, V. A. Bharadi, A. A. Agrawal, R. J. Arora , M. C. Nair, "Performance Comparison of Full

2-D DCT, 2- D Walsh and 1-D Transform over Row Mean and Column Mean for Iris Recognition", Proceedings of

ACM International Conference ICWET 2010, India, pp. 560-567, Feb. 2010MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

21-Jan-1557

[66] H. B. Kekre, T. K. Sarode, V. A. Bharadi, A. A. Agrawal, R. J. Arora , M. C. Nair, "Iris Recognition Using

Vector Quantization", Proceedings of IEEE International Conference ICSAP 2010, India, pp. 43-50,

March 2010

67 H. B. Kekre, V. A. Bharadi, "Fingerprint Orientation Field Estimation Algorithm Based on Optimized

Neighborhood Averaging", IEEE International Conference ICETET 2009, India, pp. 543-546, Dec. 2009

68 H. B. Kekre, V. A. Bharadi, "Fingerprint‘s Core Point Detection Using Orientation Field", IEEE International

Conference on Advances in Computing, Control and Telecommunication Technologies (ACT 2009), India, pp.

150 - 152 , Dec. 2009

69 H. B. Kekre, V. A. Bharadi, "Hybrid Multimodal Biometric Recognition Using Kekre‘s Wavelets, 1D Transforms &

Kekre‘s Vector Quantization Algorithms Based Feature Extraction of Face & Iris", International Journal of

Computer Application (IJCA), Vol. 3, No. 3, pp-106-115, March 2011.

70 Asim Baig, Ahmed Bouridane, Fatih Kurugollu “Fingerprint – Iris Fusion based Identification System using a

Single Hamming Distance Matcher”, 2009 Symposium on Bio-inspired Learning and Intelligent Systems for

Security.

71 H B Kekre, V A Bharadi, “Iris Recognition Using Discrete Cosine Transform and Kekre’s Fast Codebook

Generation Algorithm”, International Conference & Workshop on Emerging Trends in Technology 2010 (ICWET

2010), Mumbai, India, 26-27 Feb 2010.

72 H B Kekre, V A Bharadi, “Multimodal Biometrics: Need for Future Security Systems”

73 H B Kekre, V A Bharadi, “Multimodal Biometrics”, International Conference & Workshop on Emerging Trends in

Technology 2010 (ICWET 2010), 26-27 Feb 2010.

74 M. Tico, E. Immonen, P. Ramo, P. Kuosmanen, and J. Saarinen, ―”Fingerprint Recognition Using Wavelet

Features”, IEEE Conference on Biometrics, Vol. II, No. 8, pp. 21–24, 2001.

75 Kazuyuki Miyazawa, Koichi Ito, Takafumi Ao, Koji Kobayashi, Atsushi Katsumata, - “AN IRIS RECOGNITION

SYSTEM USING PHASE-BASED IMAGE MATCHING”, 1-4244-0481-9/06/$20.00 C2006 IEEE.

76 http://phoenix.inf.upol.cz/iris/download/

77 B.Pandya and V.Bharadi , ”Multimodal Fusion of Fingerprint & Iris using Hybrid wavelet based feature vector”, in

International Conference & Workshop on Emerging Trend in Technology Feb.2012.

78 H B Kekre, V A Bharadi, “Biometrics authentication Systems”. In journal feb,2012 published by LAP Lambert

Academic Publishing.


Publications


Journal papers

[1] B.Pandya and V.Bharadi, “Multimodal Fusion of Fingerprint& Iris using Hybrid wavelet based feature vector” inInternational journal of Applied information Systems, Feb2014.

Conference papers

[1] B.Pandya and V.Bharadi , ”Multimodal Fusion ofFingerprint & Iris using Hybrid wavelet based featurevector”, in International Conference & Workshop onEmerging Trend in Technology Feb.2012.

Book

[1] Multimodal Fusion of Iris and Fingerprint using Hybrid Wavelets Type I & Type II based Feature Vector.

-Bhavesh Pandya, Dr.Vinayak Bharadi, Dr.H.B.Kekre

Lambert Publication, Germany, ISBN No: 978-3-8484-3243-1


21-Jan-1560

Special thanks to Dr. Vinayak Bharadi

for his valuable guidance.. .!


Multimodal fusion of fingerprint and iris

Engineering

iris recognition system

biometricsbased systems

biometrics recognition

fv bhavesh h

feature vector of iris

biometric recognition

kekres wavelet

hybrid wavelet type