23496242 biometric technology

8/2/2019 23496242 Biometric Technology

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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

Dept.of.CSE M.E.S.C.E. Kuttippuram1


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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


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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23496242 biometric technology

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