Transcript
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1. INTRODUCTION
BIOMETRICS refers to the automatic identification of a person based
on his physiological / behavioral characteristics. This method of
identification is preferred for various reasons;the person to be identified is
required to be physically present at the point of identification; identification
based on biometric techniques obviates the need to remember a password or
carry a token. With the increased use of computers or vehicles of information
technology, it is necessary to restrict access to sensitive or personal data. By
replacing PINs, biometric techniques can potentially prevent unauthorized
access to fraudulent use of ATMs, cellular phones, smart cards, desktop PCs,
workstations, and computer networks. PINs and passwords may be forgotten,
and token based methods of identification like passports and drivers
licenses may be forged, stolen, or lost .Thus biometric systems of
identification are enjoying a renewed interest. Various types of biometric
systems are being used for realtime identification ; the most popular are
based on face recognition and fingerprint matching. However there are
other biometric systems that utilize iris and retinal scan, speech, facial
thermo grams, and hand geometry.
A biometric system is essentially a pattern recognition system,
which makes a personal identification by determining the authenticity of a
specific physiological or behavioral characteristics possessed by the user. An
important issue in designing a practical system is to determine how an
individual is identified. Depending on the context, a biometric system can be
either a verification (authentication) system or an identification system. There
are two different ways to resolve a persons identity : Verification and
Identification. Verification ( Am I whom I claim I am ?) involves
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confirming or denying a persons claimed identity. In Identification one has
to establish a persons identity (whom am I?). Each one of these approaches
has its own complexities and could probably be solved best by a certain
biometric system.
Biometrics is rapidly evolving technology, which is being used in
forensics such as criminal identification and prison security, and has the
potential to be used in a large range of civilian application areas . Biometrics
can be used transactions conducted via telephone and Internet (electronic
commerce and electronic banking) . In automobiles, biometrics can replace
keys with key -less entry devices.
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2. ORIGIN OF BIOMETRICS
Biometrics dates back to the ancient Egyptians, who measured people to
identity them. But automated devices appeared within living memory. One
of the first commercial devices introduced less than 30 years ago. The
system is called the indentimat . The machine measured finger length and
installed in a time keeping system. Biometrics is also catching on computer
and communication system as well as automated teller machines (ATMs).
Biometrics devices have three primary components. One is an automated
mechanism that scans and captures a digital / analog image of a living
personal characteristics. Another handles compression, processing, storage
and comparison of image with the stored data . The third interfaces with
application systems. These pieces may be configured to suit differentsituations . A common issue is where the stored image resides:on a card,
presented by the person being verified or at a host computer.
Recognition occurs when an individuals image is matched with one of a
group of stored images . This is the way the human brain performs
most day to day identifications. For the brain this is a relatively quick and
efficient process, where as for computers to recognise that a living image
matches one of many it has stored, the job can be time consuming and costly.
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3. TYPOLOGY OF BIOMETRICS
Biometrics encompasses both physiological and behavioural
characteristics. This is illustrated in Figure 1. A physiological characteristic
is a relatively stable physical feature such as finger print, hand silhouette
, iris pattern or facial features. These factors are basically unalterable
with out trauma to the individual.
A behavioral tract, on the other hand, has some physiological basis, but
also reflects persons physiological makeup. The most common trait used in
identification is a persons signature. Other behaviours used include a
persons keyboard typing and speech patterns. Because of most behavioural
characteristics change over time, many biometrics machine not rely on
behavior. It is required to update their enrolled reference template may
differ significantly from the original data, and the machine become more
proficient at identifying the person. Behavioral biometrics work bestwith regular use.
The difference between physiological and behavioral methods is
important. The degree of intrapersonal variation is smaller in physical
characteristics than in a behavioral one. Developers of behaviour-based
systems, therefore have a tougher job adjusting for an individuals
variability. However, machines that measure
physical characteristics tend to be larger and more expensive, and more
friendly. Either technique affords a much more reliable level of identification
than passwords or cards alone.
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TYPOLOGY OF IDENTIFICATION METHODS
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Characteristics
Manual and semi-
Biographics
Automated biometrics
Physiological Behavioral
Face Finger
print
Hand Eye
Signature Voice Keystroke
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4. VARIOUS BIOMETRIC SYSTEMS
4.1 HAND
The three dimensional shape of a persons hand has several
advantages as an identification device. Scanning a hand and producing a
result takes 1.2 seconds. It requires little space for data storage about 9
bytes which can fit easily magnetic strip credit cards.
Hand geometry is the grand daddy of biometrics by virtue of its 20 year
old history of live application. Over this span six hand-scan products have
been developed but one commercially viable product currently available,
the ID3D hand key is given below. This device was developed by
Recognition Systems Inc.
The user keys, in an identification code, is then positions his or her and
on a plate between a set of guidance pins. Looking down upon the hand is
a charge-coupled device (CCD) digital camera, which with the help of
mirror captures the side and top view of the hand simultaneously.
The black and white digital image is analysed by software running
on a built in HD 64180 microprocessor. ( This a Z-80 base chip ) to
extract identifying characteristics from the hand picture. The software
compares those features to captured when the user was enrolled in the
system, and signals the result-match or no match. Analysis is based on
the measurement and comparison of geometric. The magnification
factor of the camera is known and is calibrated for pixels per inch of real
distance. Then the dimensions of parts of the hand, such as finger length,
width and area are measured, adjusted according to calibration marks on the
platen and used to determine the identifying geometric of the hand.
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A strong correlation exists between the dimension of the hand. For
example if the little finger is long, the index finger will most likely also be
along. Some 400 hands were measured to determine these interrelationships,
and the results are integrated into the system as a set of matrices are applied
to measured geometric to produce the 9 byte identity feature vector that is
stored in the system during enrolment, with this amount of data
compression, the current 4.5 kg unit with single printed circuit board can
store 2000 identities.
Enrolment involves taking three hands reading and averaging the
resulting vectors. Users can enrol themselves with minimal help. When
used for identification the 9-byte vector is compared to the stored vector
and score based on the scalar difference is stored. Low scores indicate a
small difference, high scores mean a poor match. The recognition
systems product fine-tunes the reference vector a small increment at a
time, in case the original template was made under less than perfect
conditions.
There are so many other systems for hand recognition. One was an
effort by SRI international, to take pictures of unconstrained hands help
in free space. This system was introduced in 1985. Biometrics Inc.,
Tokyos Toshiba Corp. Identification corp. etc are some companies which
developed biometrics systems.
4.2 FINGER PRINT
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Perhaps most of the work in biometrics identification has gone into the
fingerprint For general security and computer access control application
fingerprints are gaining popularity.
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The fingerprints stability and uniqueness is well established. Based
upon a century of examination, it is estimated that the change of two
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people, including twins, having the same print is less than one a billion. In
verifying a print, many devices on the market analyze the position of details
called minutiae such as the endpoints and junctions of print ridges. These
devices assign locations to the minutiae using x, y, and directional
variables. Some devices also count the number of ridges between
minutiae to form the reference template. Several companies claim to be
developing templates of under 100 bytes. Other machine approach the finger
as an image processing problem and applying custom very large scale
integrated chips,neural networks, fuzzy logic and other technologies to the
matching problem.
The fingerprint recognition technology was developed for some 12
years before Being matched in 1983 by Identix Inc.
The Identix system uses a compact terminal that incorporates light
and CCD image sensors to take high-resolution picture of a fingerprint. It
based on 68000 CPU with additional custom chips, but can also be configured
as a peripheral for an IBM PC. It can operate as a standalone system or as part
of a network.
To enrol a user is assigned a personal identification number and then
puts a single finger on the glass or Plexiglas plate for scanning by a CCD
image sensor. The 250-KB image is digitalized and analyzed, and the
result is approximately 1-KB mathematical characterization of the
fingerprint. This takes about 30 seconds. Identity verifications take less
than 1 second . The equipment generally gives the user three attempts for
acceptance or finds rejection. With the first attempt the false rejection is
around 2-3 percent and false acceptance is less than 0.0001 per cent. Each
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standalone unit cab stores 48 fingerprint templates which may be expanded to
846 by installing an additional memory package.
Fingerprints have overcome the stigma of their use in law enforcement
and military applications. Finger print recognition is appropriate for many
applications and is
familiar idea to most people even if only from crime dramas on
television. It is non-intrusive, user friendly and relatively inexpensive.
4.3. FACE
Biometrics developers have also not lost sight of fact that humans
use the face as their primary method of telling whos who. More than a
dozen effort to develop automated facial verification or recognition systems
use approaches ranging from pattern recognition based on neural networksto infrared scans of hot spots on the face.
Using the whole face for automatic identification is a complex
task because its appearance is constantly changing. Variations in facial
expressions, hair styles and facial hair, head position, camera scale and
lighting create image that are usually different from the image captured on a
film or videotape earlier. The application of advanced image processing
techniques and the use of neural networks for classifying the images,
however, has made the job possible.
Artificial neural networks are massively connected parallel
networks of simple computing elements. Their design mimics the
organization and performance of biological neural networks in the nervous
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system and the brain. They can learn and adapt and be taught to recognize
patterns both static and dynamic. Also their interconnected parallel
structure allows for a degree of fault tolerance as individual computing
elements become inoperative. Neural networks are being used for
pattern recognition function approximation, time series analysis and disk
control.
There is only one system available on the market today. The system is
developed by Neuro Metric Vision system Inc. this can recognize faces
with a few constraints as possible, accommodating a range of camera
scales and lighting environments, along with changes in expression and
facial hair and in head positions. The work sprang from the realisation that
such techniques as facial image comparisons, measurement of key facial
structure and the analysis of facial geometry could be used in face
recognition system. Any of these approaches might employ rule-based logic
or a neural network for the image classification process.
The Nuerometric system operates on an IBM-compatible 386 or
486 personal computer with a maths co-processor, a digital signal
processing card and a frame grabber card to convert raster scan frames
from an attached camera in to pixel representations. The system can
capture images from black and white video cameras or vide recorders
in real time.
Software running on the DSP card locates the face in the video
frame, scales and rotates if necessary, compensating for lighting
differences and performs mathematical transformations to reduce the
face to a set of floating point feature vectors. The feature vector set is input
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to the neural network trained to respond by matching it to one of the trained
images in as little as 1 seconds.
The systems rejection level can be tuned by specifying the different
signal to noise ratios for the match a high ratio to specify a precise
match, and a lower one to allow more facial variation. In a tightly
controlled environment, for example, the system could set up to recognise
a person only when looking at the camera with same expression he or she
had when initially enrolled in the system.
To enrol someone in the Neuro Metric system, the face is captured,
the feature vectors extracted, and the neural network is trained on the features.
Grayscale facial images may be presented from live video or photographs
via videodisk. The neural network is repeatedly trained until it learns all the
faces and consistently identifies every image. The system uses neural
network clusters of 100-200 faces to build its face recognition database. If
multiple clusters are required they can be accessed sequentially or
hierarchically. When faces are added to or detected from the database,
only the affected clusters must be retrained, which takes 3-5 minutes.
4.4 EYE
The other method of identification involves the eye. Two types of eye
identification are possible, scanning the blood vessel pattern on the retina
and examining the pattern of the structure of the iris. Now we can look
through a detailed description of each type below.
4.4 1 RETINA
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Retina scans, in which a weak infrared light is directed through the
pupil to the back of the eye, have been commercially available since 1985.
The retinal pattern is reflected back to a charge-coupled device (CCD)
Camera, which captures the unique pattern and represents it in less than 35
bytes of information. Retina scans are one of the best biometrics
performers on the market, with low false reject rates and nearly 0 present
false accept rate. The technology also offers small data templates provides
quick identity confirmations, and handles well the job of recognizing
individuals in a database of under 500 people. The toughest hurdle for
retinal scan technology is user resistance. People dont want to put their eye
as close to the device as necessary. Only one company, Eyedentyfy
Inc., produces retinal scan products.
4.4 2 IRIS
Once it was the whites of their eyes that counted. Retinal pattern
recognition has been tried but found uncomfortable because the individual
must touch or remain very close to a retinal scanner. Now the iris is
the focus of a relatively new biometrics means of identification. Standard
monochrome video or photographic technology in combination with
robust software and standard video imaging techniques can accept or reject
an iris at distance of 30-45 cm.
A device that examines the human iris is being developed by
Iriscan Inc. The techniques big advantage over retinal scans is that it does
not require the user to move close to the device and focus on a target
because the iris pattern is on the eyes surface. In fact the video image of an
eye can be taken at distance of a metre or so, and the user need not interact
with device at all.
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The technology being implemented by Iriscan Inc., is based on
principles developed and planted by ophthalmologists Leonard Flom and
Aran Safir and on mathematical algorithms developed by John Daugman. In
their practice, Flom and Safir observed that every iris had highly detailed
and unique texture that remains stable over decades of life. This part of the
eye is one of the most striking features of the face. It is easily visible from
yards away a s a coloured disk, behind the clear protective window of the
cornea, surrounded by the white tissue of the eye. Observable features
include contraction furrows striations, pits, collagenons fibres, filaments,
crypts, serpentine, vasculature, rings and freckles. The structure of iris is
unique, as in fingerprint, but it boasts more than six times as many
distinctly different characteristics as the finger print. This part of the eye,
moreover cannot surgically modified without damage to vision. It is
produced from damage or internal changes by the cornea and it responds
to light, a natural test against artifice.
4.5 SPEECH
Another biometrics approach that is attractive because of its acceptability
to users is voice verification. All the systems used in analyzing the voice
are rooted in more broadly based speech processing technology. Currently,
voice verification is being used in access control for medium security areas
or for situations involving many people as in offices and lab. There are two
approaches to voice verification. One is using dedicated hardware and
software at the point of access .The second approach is using personal
computer host configurations that drives a network over regular phone lines.
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One of the latest implementation of the technology is the recently
demonstrated AT&T Smart Card used in an automatic teller system. The
AT&T prototype stores an individuals voice pattern on a memory card, the
size of a credit card. In brief, someone opening an account at a bank has to
speak a selected two or three-syllable word eight items. The word can be
chosen by the user and belong to any language or dialect.
Another approach being as an alternative to the algorithms
discussed is based on Hidden Markov Models, which consider the probability
of state changes and allow the system to predict what the speaker is
trying to say. This capability would be crucial for speaker independent
recognition. Storing voice templates on a card and receiving and processing
voice information at a local device, such as ATM, eliminated variations due to
telephone connection and types of telephones used.
4.5.1 SPEAKER VERIFICATION
The speaker- specific characteristics of speech are due to differences in
physiological and behavioral aspects of the speech production system in
humans. The main physiological aspect of the human speech production
system is the vocal tract shape. The vocal tract is generally considered as
the speech production organ above the vocal folds, which consists of the
following: (a) laryngeal pharynx ( beneath the epiglottis), (b) oral pharynx
( behind the tongue, between the epiglottis and velum ), ( c) oral cavity
( forward of the velum and bounded by the lips, tongue, and palate ), (d) nasal
pharynx ( above the velum, rear end of nasal cavity ), and (e) nasal cavity
(above the palate and extending from the pharynx to the nostrils ). The shaded
area in figure 4 depicts the vocal tract.
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Figure 4
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The vocal tract modifies the spectral content of an acoustic wave
as it passes through it, thereby producing speech. Hence, it is common in
speaker verification systems to make use of features derived only from
the vocal tract. In order to characterize the features of the vocal tract, the
human speech production mechanism is represented as a discrete-time system
of the form depicted in figure 5.
Figure 5.
The acoustic wave is produced when the airflow from the lungs is
carried by the trachea through the vocal folds. The source of excitation can
be characterized as phonation, whispering, friction, compression, vibration, or
a combination of these. Phonated excitation occurs when the airflow is
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modulated by the vocal folds. Whispered excitation is produced by airflow
rushing through a small triangular opening between the arytenoids cartilage at
the rear of the nearly closed vocal folds. Friction excitation is produced by
constrictions in the vocal tract. Compression excitation results from
releasing a completely closed and pressurized vocal tract. Vibration
excitation is caused by air being forced through a closure other than the vocal
folds, especially at the tongue. Speech produced by phonated excitation is called
voiced, that produced by phonated excitation plus friction is called mixed
voiced, and that produced by other types of excitation is called unvoiced.
It is possible to represent the vocal-tract in a parametric form as the
transfer function H (z). In order to estimate the parameters of H (z) from
the observed speech waveform, it is necessary to assume some form for H (z) .
Ideally, the transfer function should contain poles as well as zeros. However,
if only the voiced regions of speech are used then an all-pole model for H (z) is
sufficient. Furthermore, linear prediction analysis can be used to efficiently
estimate the parameters of an all-pole model. Finally, it can also be noted that
the all-pole model is the minimum-phase part of the true model and has an
identical magnitude spectra, which contains the bulk of the speaker-dependent
information.
4.6 MULTI BIOMETRICS
4.6.1 Integrating Faces and Fingerprints for Personal
Identification
An automatic personal identification system based on
fingerprints or faces is often not able to meet the system performance
requirements. Face recognition is fast but not reliable while fingerprint
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verification is reliable but inefficient in database retrieval. A prototype
biometric system is developed 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
schema 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.
4.6.2 A Multimodal Biometric System Using Fingerprint, Face
and Speech
A biometric system which relies only on a single biometric
identifier in making a personal identifications often not able to meet the
desired performance requirements. Identification based on multiple
biometrics represents on emerging trend. A multimodal biometric system
is introduced (figure given below ), which integrates face recognition,
fingerprint verification, and speaker verification in making a personal
identification.
This system takes advantage of the capabilities of each individual
biometric. It can be used to overcome some of the limitations of a single
biometrics. Preliminary experimental results demonstrate that the identity
established by such an integrated system is more reliable than the identity
established by a face recognition system, a fingerprint verification system and
a speaker verification system.
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Figure 6
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5. CONCLUSION
A range of biometric systems are in developments or on the market
because no one system meets all needs. The trade off in developing these
systems involve component cost, reliability, discomfort in using a device, the
amount of data needed and other factors. But the application of advanced
digital techniques has made the job possible. Further experiments are going
all over the world. In India also there is a great progress in this field. So we
can expect that in the near future itself, the biometric systems will become
the main part in identification purposes.
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6. REFERENCES
1. HTTP:/BIOMETRICS.CSE.MSU./
2. BIOMEDICAL INSTRUMENTATION W.H. CROWELL
3. PENSTROKES AUGUST 2002
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ABSTRACT
BIOMETRICS refers to the automatic identification of a person based
on his or her physiological or behavioral characteristics like fingerprint,
or iris pattern, or some aspects of behaviour like handwriting or
keystroke patterns. Biometrics is being applied both to identity
verification. The problem each involves is somewhat different.
Verification requires the person being identified to lay claim to an identity.
So the system has two choices, either accepting or rejecting the personsclaim. Recognition requires the system to look through many stored sets of
characteristics and pick the one that matches the unknown individual
being presented. BIOMETRIC system is essentially a pattern recognition
system, which makes a personal identification by determining the
authenticity of a specific physiological or behavioral characteristics
possessed by the user.
Biometrics is a rapidly evolving technology, which is being
used in forensics Such as criminal identification and prison security, and
has the potential to be used in a large range of civilian application
areas. Biometrics can be used transactions conducted via telephone and
Internet (electronic commerce and electronic banking. In automobiles,
biometrics can replace keys with key-less entry devices
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ACKNOWLEDGEMENTS
I express my sincere thanks to Prof. M.N Agnisarman
Namboothiri (Head of the Department, Computer Science and Engineering,
MESCE), Mr. Zainul Abid (Staff incharge) for their kind co-operation for
presenting the seminar.
I also extend my sincere thanks to all other members of the faculty of
Computer Science and Engineering Department and my friends for their co-
operation and encouragement.
SAJEEV PB
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CONTENTS
Chapter Title page
1 INTRODUCTION 1
2 ORIGIN OF BIOMETRICS 3
3 TYPOLOGY OF BIOMETRICS 4
4 VARIOUS BIOMETRIC SYSTEMS 6
4.1 HAND 6
4.2 FINGERPRINT 8
4.3 FACE 11
4.4 EYE 13
4.5 SPEECH 15
4.6 MULTI BIOMETRICS 19
5 CONCLUSION 22
6 REFERENCES 23
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