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Biometric Technology Seminar Report 2003

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 driver’s 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 real–time

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 person’s identity :

Verification and Identification. Verification ( Am I whom I claim I am ?)

Dept.of.CSE M.E.S.C.E. Kuttippuram 1


involves confirming or denying a person’s claimed identity. In

Identification one has to establish a person’s 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.



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

(ATM’s).

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 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 individual’s 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.



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 person’s physiological makeup. The most common trait used

in identification is a person’s signature. Other behaviours used include a

person’s 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 individual’s

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.



TYPOLOGY OF IDENTIFICATION METHODS


Characteristics

Manual and semi-automated biometrics

Biographics

Automated biometrics

Physiological Behavioral

Face Finger print

Hand Eye

Signature Voice Keystroke


4. VARIOUS BIOMETRIC SYSTEMS

4.1 HAND

The three dimensional shape of a person’s 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.





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., Tokyo’s 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.





The fingerprint’s 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

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 who’s who. More than a

dozen 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.

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 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 to the neural network trained to respond by matching it to one of the

trained images in as little as 1 seconds.



The system’s 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

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 don’t 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 technique’s 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 eye’s 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.



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.



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 individual’s 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.



Figure 4



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 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

4.6.1Integrating 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. 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.



Figure 6



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.



6. REFERENCES

1. HTTP:/BIOMETRICS.CSE.MSU./

2. BIOMEDICAL INSTRUMENTATION W.H. CROWELL

3. PENSTROKES AUGUST 2002



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 person’s 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.

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.

SAJEEV PB



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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