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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
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am ?) involves 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 interfaceswith application systems. These pieces may be configured to suit different
situations . 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 best with 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
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. Thesoftware 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
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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 lessthan 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
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
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
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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 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 adozen effort to develop automated facial verification or recognition systems
use approaches ranging from pattern recognition based on neural networks
to 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.
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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 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 keyfacial 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
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differences and performs mathematical transformations to reduce the
face to a set of floating point feature vectors. The feature vector set is
input 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 untilit 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.
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4.4 1 RETINA
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
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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 againstartifice.
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 ),
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(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.
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.
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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 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 thetransfer 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
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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
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. Itmeets 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.
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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.
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 personbased 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 persons claim. 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.
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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
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.
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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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