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BIOMETRICS

KAMALA INSTITUTE OF TECHNOLOGY & SCIENCE

SINGAPUR, HUZURABAD

2008-2009



ABSTRACT

Biometrics refers to the automatic

identification of a person based on his/her

physiological or behavioral characteristics.

This method of identification is preferred

over traditional methods involving

password’s and PIN numbers.

Using Biometrics you can unlock

your houses, withdrawing money from a

bank with just a blink of an eye, a tap of

your finger or by just showing your face.

Fingerprint recognition is most widely

accepted biometric among the technology

being used today. Biometrics in Face

recognition has received a surge of

attention since of disaster of 11/9 for its

ability to identify known terrorists and

criminals.

First usage:-

First used in China in the 14th

century -- merchants stamped hand and

foot prints of children on paper with ink to

distinguish one child from another. In

Europe, Richard Edward Henry developed

fingerprinting for Scotland Yard in the

later half of the 19th century.

INTRODUCTION

Definition: Biometrics is the process by

which distinguishing human anatomy is

used for identification and verification.

Biometric systems are pattern recognition

systems which are automated methods of

determining the authenticity of a specific

physiological or behavioral characteristic

possessed by the user to determine or

verify identify. The word biometrics

comes from the Greek words bio -

meaning life, and metric - meaning to

measure.

Identification is determining who a

person is. One has to establish a person's

identity (Who am I?).

Verification involves confirming or

denying a person's claimed identity. (Are

you who you claim to be?).

Using Biometrics for identification, for

example, DNA evidence or fingerprints,

has been used for many years. It is widely

accepted as being credible and accurate.

Biometrics is now being used for

verification as part of security systems.

Verification has been part of security

systems for many years. We have been

asked for physical items of identification

like a license or passport. We have also

been asked for things we know to verify

our identity, like a pin number or a maiden



name. Unfortunately, security systems that

use physical forms of identification and

personal information can be easily

"fooled". It is the increasing need for more

security that has enabled biometrics to

evolve to its current state of development.

Biometrics determines who we are using

our distinct physical features.

PARTS OF BIOMETRICS

Biometric systems consist of three parts:

1.Scanner:

Scans the anatomy being used.

2.Software:

The software which gathers the

information and converts it to digital form

3.Database:

The database which compares the current

data to a stored database and determines

authenticity and identification.

CLASSIFICATION

Biometrics is classified into two

categories:

1.Physiological

Physiological biometrics measures the

distinct traits that people have, usually (but

not always or entirely) dictated by their

genetics. They are based on measurements

and data derived from direct measurement

of a part of the human body.

Physiological Biometric Applications:

• Fingerprints

• Retina Scans

• Hand Geometry

• Facial Patterns

• DNA

2.Behavioral

Behavioral biometrics measure the distinctactions that humans take, which are

generally very hard to copy from one

person to another. They measure

characteristics of the human body

indirectly.

Behavioral Biometric Applications:

• Speaker Recognition

• Gait

• Signature

• Keystroke

Physiological

1. Fingerprints



Fingerprinting takes an image

(either using ink or a digital scan) of a

person's fingertips and records its

characteristics. Most developed and widely

accepted type of biometric. It uses the

patterns of whorls, arches, and loops along

with patterns of ridges, furrows and

minutise. Fingerprints can be compared to

a database to determine identification.

Fingerprinting was first used in this

country by the New York civil service

since 1902 and was first discussed in the

scientific community and proposed as a

means of identification in 1880. No twohumans have ever been found to have the

same fingerprint. Even identical twins, that

are twins that resulted from one embryo

that divided to make two offspring, have

unique fingerprints. In 2000, electronic

fingerprints were introduced for

verification of the user during computer

login procedures.

Pros:

• No image of the fingerprint is

actually created, therefore no

means to "steal fingerprints".

• Takes less than 5 seconds.

• To prevent fake fingers, many

systems also measure blood flow.

• 1:5000 accuracy for a single finger

•Using multiple fingers increasesaccuracy exponentially.

• Small storage space required for

biometric template.

• Fingerprinting would be a very

good form of biometrics to use in

schools as fingerprint patterns are

set at birth and do not change asthe individual grows.

Cons:

• Fingerprints can be "lifted" very

easily by using a dusting powder

and tape as is done routinely in

crime scene investigations.

Scanners can easily be fooled by

using a similar technique.

2. Retina Scan:

The retina is the thin layer of cells at the

back of the eyeball that transmits images

into neural signals. The retina is very rich

in blood vessels and each individual has a

distinct pattern of blood vessels on their

retina. Retina scans can compare the



patterns to a known data base to confirm

identification of an individual.

Retinal scans take 10 to 15

seconds. Development on retinal scans

began in the 1930's. The first device

available for commercial use was in 1984

and was called the Eyedentification 7.5

personal identification unit, developed by

EyeDentify founded in 1976.

Pros:

• Retina scan devices are one of the

most accurate biometric

applications available today as

retinal patterns remain constant

throughout an individual's life.

Cons:

• Requires user to remove glasses to

ensure that the user can place their

eye close to the device.

• General public perceives the laser

as being potentially harmful to the

eye.

• Hardware is expensive.

• System not easy to use.

3.Voice:

With voice recognition, the user speaks

into a microphone his password or access

phrase. Verification time is approximately

5 seconds.

Based on physiological and

behavioral differences in speech as the

sound waves are produced. Physiological

differences arise from differences in

human's vocal tract. These includestructural differences in the pharynx, oral

and nasal cavities as well as the shape of

the vocal cords.

Pros:

• Easy to use and takes

approximately 5 seconds for

verification.

Cons:

• To prevent compromise of the

system by use of a recorded voice,

the majority of voice devices

require the high and low

frequencies of the sound to match.



This is difficult to recreate well for

many recording instruments.

4.Facial:

Facial recognition began development in

the late 1980's. Facial recognition analyzes

facial images using a video camera. It

measures structure like distance between

the eyes, between the eyes and nose, eyes

and mouth, nose and mouth, etc. Users

stand approximately 3 feet from the

camera and are often required to blink or

smile to prevent successful use of fake

faces.

Pros:

• Non-intrusive, users stand several

feet away. Not required to wait for

long periods of time.

Cons:

• Non-intrusive nature gives a sense

of "Big Brother".

5. Hand Geometry:

Involves the measurement and

analysis of the shape of an individual's

hand. User places hand on a metal

template. Process takes approximately 5

seconds. Has been used for 20 years.

Pros:

• Special hardware required can be

easily integrated with other

biometric systems like

fingerprinting.

• Easy for users to use the system.

• Resistant to tampering since the

effort required to make a fake hand

is not worth the effort.

Cons:



• Hand geometry is not unique to an

individual, therefore possible to

have duplicate readings.

•

Hardware is costly and takes upmuch space.

• Injuries to hands cause problems

with the system.

6. Gait:

Gait is the peculiar way one walks

and is a complex spatio-temporal

behavioral biometrics. Gait is not

supposed to be unique to each individual,

but is sufficiently characteristic to allow

identity authentication. Gait is a behavioral biometric and may not stay invariant

especially over a large period of time, due

to large fluctuations of body weight, major

shift in the body weight (e.g., waddling

gait during pregnancy, major injuries

involving joints or brain (e.g., cerebellar

lesions in Parkinson disease), or due to

inebriety (e.g., drunken gait).

Humans are quite adept at

recognizing a person at a distance from his

gait. Although, the characteristic gait of a

human walk has been well researched in

the Department of Immigration and

Naturalization in the United States

specifically requests photographs of

individuals with clearly visible right ear.

Biomechanics community to detect

abnormalities in lower extremity joints, the

use of gait for identification purposes isvery recent. Typically, gait features are

derived from an analysis of video-

sequence footage of a walking person and

consist of characterization of several

different movements of each articulate

joint. Currently, there do not exist any

commercial systems for performing gait-

based authentication. The method of input

acquisition for gait is not different from

that of acquiring facial pictures, and hence

gait may be an acceptable biometric. Since

gait determination involves processing of

video, it is compute and input intensive.

7. DNA:



DNA (DeoxyriboNucleic Acid) is

the one-dimensional ultimate unique code

for one's individuality - except for the fact

that identical twins have the identical

DNA pattern. It is, however, currently

used mostly in the context of forensic

applications for identification.

Three issues limit the utility of this

biometrics for other applications:

(i) Contamination and sensitivity: It is

easy to steal a piece of DNA from an

unsuspecting subject to be subsequently

abused for an ulterior purpose;

(ii ) Automatic real-time identification

issues: The present technology for genetic

matching is not geared for online

unobtrusive identifications. Most of the

human DNA is identical for the entire

human species and only some relatively

small number of specific locations

(polymorphic loci) on DNA exhibit

individual variation. These variations are

manifested either in the number of

repetitions of a block of base sequence

(length polymorphism) or in the minor non-functional perturbations of the base

sequence (sequence polymorphism). The

processes involved in DNA based personal

identification determine whether two DNA

samples originate from the same/different

individual(s) based on the distinctive

signature at one or more polymorphic loci.

A major component of these processes

now exist in the form of cumbersome

chemical methods (wet processes)

requiring an expert's skills. There does not

seem to be any effort directed at a

complete automation of all the processes.

iii) Privacy issues: Information about

susceptibilities of a person to certain

diseases could be gained from the DNA

pattern and there is a concern that the

unintended abuse of genetic code

information may result in discrimination ine.g., hiring practices.

Important points to observe in DNA:

• Only 2-3% of the DNA sequence

represents the known genetic

material.

• Almost 70% of the sequence is

composed of non-coding regions,

i.e. we do not know the function of

these regions.

• Almost 30% of the sequence is

composed of non-coding repetitive

DNA, and only 1/3 is tandemly

repetitive, the rest (2/3) is

randomly repetitive.

8. Signature and Acoustic Emissions:



The way a person signs her name is known

to be a characteristic of that individual.

Although signatures require contact and

effort with the writing instrument, they

seem to be acceptable in many

government, legal, and commercial

transactions as a method of personal

authentication.

Signatures are a behavioral biometric, evolve over a period of time

and are influenced by physical and

emotional conditions of the signatories.

Signatures of some people vary a lot: even

the successive impressions of their

signature are significantly different.

Further, the professional forgers can

reproduce signatures to fool the unskilled

eye. Although, the human experts can

discriminate genuine signatures from the

forged ones, modeling the invariance in

the signatures and automating signature

recognition process are challenging. There

are two approaches to signature

verification: static and dynamic. In static

signature verification, only geometric

(shape) features of the signature are used

for authenticating an identity.

Typically, the signature

impressions are normalized to a known

size and decomposed into simple

components (strokes). The shapes and

relationships of strokes are used as

features. In dynamic signature verification,

not only the shape features are used for

authenticating the signature but thedynamic features like acceleration,

velocity, and trajectory profiles of the

signature are also employed. The signature

impressions are processed as in a static

signature verification system. Invariants of

the dynamic features augment the static

features, making forgery difficult since the

forger has to not only know the impression

of the signature but also the way the

impression was made. A related

technology is authentication of an identity

based on the characteristics of the acoustic

emissions emitted during a signature

scribble.

9. Keystroke Dynamics:

Keystroke dynamics is an automated

method of examining an individual’s

keystrokes on a keyboard. This technology

examines such dynamics as speed and

pressure, the total time taken to type

particular words, and the time elapsed



between hitting certain keys. This

technology’s algorithms are still being

developed to improve robustness and

distinctiveness. One potentially useful

application that may emerge is computer

access, where this biometric could be used

to verify the computer user’s identity

continuously.

DETAILED DISCRIPITION ON FACE

RECOGNITION:

Face recognition:

As a biometric, facial recognition is a form

of computer vision that uses faces to

attempt to identify a person or verify a

person’s claimed identity. Regardless of

specific method used, facial recognition is

accomplished in a five step process.

1. First, an image of the face is acquired.

This acquisition can be accomplished by

digitally scanning an existing photograph

or by using an electro-optical camera to

acquire a live picture of a subject. As

video is a rapid sequence of individual still

images, it can also be used as a source of

facial images.

2. Second, software is employed to detect

the location of any faces in the acquired

image. This task is difficult, and often

generalized patterns of what a face

“looks like” (two eyes and a

mouth set in an oval shape) are

employed to pick out the faces.

3. Once the facial detection

software has targeted a face, it

can be analyzed. As noted in slide

three, facial recognition analyzes

the spatial geometry of

distinguishing features of the

face. Different vendors use

different methods to extract the

identifying features of a face.

Thus, specific details on the

methods are proprietary. The

most popular method is called

Principle Components Analysis

(PCA), which is commonly

referred to as the eigenface

method. PCA has also been

combined with neural networks

and local feature analysis in

efforts to enhance its

performance. Template

generation is the result of the

feature extraction process. A



template is a reduced set of data

that represents the unique

features of an enrollee’s face. It is

important to note that becausethe systems use spatial geometry

of distinguishing facial features,

they do not use hairstyle, facial

hair, or other similar factors.

4. The fourth step is to compare

the template generated in step

three with those in a database of

known faces. In an identification

application, this process yields

scores that indicate how closely

the generated template matches

each of those in the database. In

a verification application, the

generated template is only

compared with one template in

the database – that of the claimed

identity.

5. The final step is determining

whether any scores produced in

step four are high enough to

declare a match. The rules

governing the declaration of a

match are often configurable by

the end user, so that he or she

can determine how the facial

recognition system should behave

based on security and operational

considerations.

This graphic depicts a notional

facial recognition surveillance

system. Read clockwise from thelower left-hand corner, this

system identifies and locates

“targets” (e.g., criminals, suspect

terrorists, missing children)

through a networked system of

surveillance cameras (or CCTV).

Video streams are sent over anetwork to a central control

facility (e.g., “Control Room”). At

that central facility, computers

find faces in the video, and then

attempt to find a match in a

database of target individuals. If a

probable match is found, thesystem alerts an officer; it

presents him with the image of

the suspected match, as well as

the image of the individual in the

database. This verification step

uses trained officers to ensure

that false alarms generated by

the system are caught and



recorded. If the officer decides

that the match is not a false

alarm, he forwards the alert to

officers on patrol, who are in thevicinity of where the original

camera filmed the suspect.

People are generally very good at

recognizing faces that they know.

However, people experience

difficulties when they perform

facial recognition in surveillance

or watch post scenario. Several

factors account for these

difficulties: most notably, humans

have a hard time recognizing

unfamiliar faces. Combined with

relatively short attention spans, it

is difficult for humans to pick out

unfamiliar faces. Considerable

evidence supports this claim. For

example, in a British study,

trained supermarket cashiers

were tested on their ability to

screen shoppers using credit

cards that included a photograph

of the card owner. Each shopper

was issued four cards: one with arecent picture of the shopper, one

that included minor modifications

to the shopper’s hairstyle, facial

hair or accessories (e.g., glasses,

hat), another card with a

photograph of a person similar in

appearance to the shopper, and

the last card with a photograph of

a person who was only of the

same sex and race as the

shopper. When the various cards

were presented to the checkout

clerks, more than half of the

fraudulent cards were accepted.

The breakdown was as follows: 34

percent of the cards that did not

look like the shopper were

accepted, 14 percent of the cards

where the appearance had been

altered were accepted, and 7

percent of the unchanged cards

were rejected by the clerks. In

addition to unfamiliar face

recognition problems, the ability

of human beings to detect critical

signals drops rapidly from the

start of a task, and stabilizes at a

significantly lower level within 25

to 35 minutes. Thus the ability of



people to focus their attention

drops significantly after only half

an hour.

People are generally very good atrecognizing faces that they know.

However, people experience difficulties

when they perform facial recognition in

surveillance or watch post scenario.

Several factors account for these

difficulties: most notably, humans have a

hard time recognizing unfamiliar faces.

Combined with relatively short attention

spans, it is difficult for humans to pick out

unfamiliar faces. Considerable evidence

supports this claim. For example, in a

British study, trained supermarket cashiers

were tested on their ability to screen

shoppers using credit cards that included a

photograph of the card owner. Each

shopper was issued four cards: one with a

recent picture of the shopper, one that

included minor modifications to the

shopper’s hairstyle, facial hair or

accessories (e.g ., glasses, hat), another

card with a photograph of a person similar

in appearance to the shopper, and the last

card with a photograph of a person who

was only of the same sex and race as the

shopper. When the various cards were

presented to the checkout clerks, more

than half of the fraudulent cards were

accepted. The breakdown was as follows:

34 percent of the cards that did not look

like the shopper were accepted, 14 percent

of the cards where the appearance had

been altered were accepted, and 7 percent

of the unchanged cards were rejected by

the clerks. In addition to unfamiliar face

recognition problems, the ability of human

beings to detect critical signals drops

rapidly from the start of a task, and

stabilizes at a significantly lower level

within 25 to 35 minutes. Thus the ability

of people to focus their attention drops

significantly after only half an hour.

Machines also experience difficulties

when they perform facial recognition in

surveillance or watch post scenario. Dr.

James L. Wayman, a leading biometrics



expert, has explained that performing

facial recognition processes with relatively

high fidelity and at long distances remains

technically challenging for automated

systems. At the most basic level, detecting

whether a face is present in a given

electronic photograph is a difficult

technical problem. Dr. Wayman has noted

that subjects should ideally be

photographed under tightly controlled

conditions. For example, each subject

should look directly into the camera and

fill the area of the photo for an automated

system to reliably identify the individual

or even detect his face in the photograph.

Thus, while the technology for facial

recognition systems shows promise, it is

not yet considered fully mature. The

“Facial Recognition Vendor Test 2000”

study makes clear that the technology is

not yet perfected. This comprehensive

study of current facial recognition

technologies, sponsored by the

Department of Defense (DoD)

Counterdrug Technology Development

Program Office, the Defense Advanced

Research Projects Agency (DARPA), and

the National Institute of Justice, showed

that environmental factors such as

differences in camera angle, direction of

lighting, facial expression, and other

parameters can have significant effects on

the ability of the systems to recognize

individuals.

By controlling a person’s facial

expression, as well as his distance from the

camera, the camera angle, and the scene’s

lighting, a posed image minimizes the

number of variables in a photograph. This

control allows the facial recognition

software to operate under near ideal

conditions – greatly enhancing its

accuracy. Similarly, using a human

operator to verify the system’s results

enhances performance because the

operator can detect machine-generated

false alarms.



An “obvious” point that needs stating: The

better the quality of the captured image

and the database images, the better the

facial recognition system will perform.

The “facetrap” triangle above

demonstrates this point, with respect to

acquiring high-quality images of the

target’s face. It is difficult to acquire an

image if the authorities only know that a

suspect might be at an airport west of the

Mississippi River. It is easier to capture

the image at a facetrap. For example, a

surveillance camera can more easily

capture images of people at the check-in

counter. Sometimes facetraps can be

designed to take advantage of people’s

inclinations. For example, a person going

up an escalator will naturally look at a red

flashing light above a clock at the top of

the escalator. A surveillance camera

located there can easily capture an image;

the face has been trapped. A camera

located at a metal detector also takes

advantage of a facetrap. The best facetrap

is the one shown at the apex of the triangle

—an image captured under tightly

controlled conditions.

The following factors need to be

considered with respect to testing and

evaluation of facial recognition systems:

1. Testing should be conducted by

independent organizations that will not

reap any benefits should one system

outperform another (i.e. no conflicts of

interest involved). The Facial Recognition

Vendor Test (FVRT) testing which

government agencies sponsor is likely to be very objective.

2. The test philosophy must be considered.

For example, the FVRT tries to make the

test neither too difficult nor too easy, as it

does not want all the systems’

performance to cluster at one end of the

spectrum. The FVRT also wants to

distinguish performance of systems and

give feedback to designers for

improvement. But a drawback here is that

real life data does not present itself this

way. Performance in real life may very

well prove that none of the systems are

useful.



3. Vendors and developers should not

know test data beforehand; otherwise, they

may be tempted to fine-tune their

technology’s performance to the specific

test data. Performance data that has been

fine-tuned to specific test data is not

representative of the general performance

of the technology being tested.

4. Testing and evaluation should be

repeatable. That is, statistically similar

results should be able to be reproduced.

In the final analysis, real life

deployments will be the ultimate tests of

FR systems. For now the jury is still out on

the effectiveness of facial recognition

systems, however, the technology is

improving. Facial recognition systems

may yet become a part of our daily lives as

they improve and if they become more

acceptable, much as CCTV or surveillance

camera systems have become.

Leading products in biometrics:

Biometric is a new but promising

technology and therefore a number of

companies have appeared in the market in

a very short period of time. Some of those

products are:

Conclusion:

Biometrics is the most sought after branch today and is widely renowned for

its various applications in different fields

especially for the sake of security. It is

predominantly used to identify the

individuals in order to know their identity

by using the various techniques that were

just explained above in order to create

better security. The advances in accuracy

and usability and decreasing cost have

made the biometric technology a secure,

affordable and cost effective way of

identifying individuals. Thus helping the

people to create more safe and secure

environment especially in today’s

hazardous situations.



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