FACE RECOGNITION USING NEURAL NETWORK

A

SEMINAR REPORT

Submitted for the Partial fulfillment of the requirement of the

Degree

Of

BACHELOR OF TECHNOLOGY

in

ELECTRICAL ENGINEERING

Submitted To: Submitted By:

Prof. Navneet Maru Mr. Nikhil Mathur

(Sr. Lecturer ) ( IV B.Tech., VIII Sem.)

Department of Electrical Engineering

Jodhpur Institute of Engineering & Technology,

JIET Group of Institutions,

Rajasthan Technical University, Jodhpur (Raj.)

2011

1

CERTIFICATE

This is to certify that the Seminar Report entitled “FACE RECOGNITION

USING ARTIFICIAL NEURAL NETWORK” being submitted by Mr.

Nikhil Mathur (IV B. Tech., VIII Sem.) for the partial fulfillment of the

requirement of the Degree of Bachelor of Technology in Electrical Engineering

of Jodhpur Institute of Engineering & Technology, Jodhpur is a record of the

Seminar delivered by him.

Prof. Kusum Agarwal

(Head, EE)

Date:

Place: Jodhpur

2

ACKNOWLEDGEMENT

The compilation of this seminar would not have been possible without the support

and guidance of the following people and organization .With my deep sense of gratitude ,I

think my respected teachers for supporting this topic of my seminar. This seminar report

provides me with an opportunity to put into knowledge of advanced technology. I thereby

take the privilege opportunity to thank my guide and my friends whose help and guidance

made this study a possibility.

As a student, I learnt many things but unless I put all with the practical knowledge

as to how things really work and what are the problems generally arise, I cannot expect to

be an efficient student. So I think summer project is an indispensable part of the course.

His dedication & sincerity towards the project helped me a lot in completion of

project report and gave it the present attractive look.

Last but not the least, I would again like to express my sincere thanks to all project

guides for their constant friendly guidance during the entire stretch of this report. Every new

step I took was due to their persistent enthusiastic backing and I acknowledge this with a

deep sense of gratitude.

Mr. Nikhil Mathur

( IV B.Tech. , VIII Sem.)

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ABSTRACT

The information age is quickly revolutionizing the way transactions

are completed. Everyday actions are increasingly being handled electronically,

instead of with pencil and paper or face to face. This growth in electronic

transactions has resulted in a greater demand for fast and accurate user

identification and authentication. Access codes for buildings, banks accounts

and computer systems often use PIN's for identification and security

clearances.

Using the proper PIN gains access, but the user of the PIN is not verified.

When credit and ATM cards are lost or stolen, an unauthorized user can often

come up with the correct personal codes. Despite warning , many people

continue to choose easily guessed PIN's and passwords: birthdays, phone

numbers and social security numbers. Recent cases of identity theft have

hightened the nee for methods to prove that someone is truly who he/she

claims to be.

Face recognition technology may solve this problem since a face is

undeniably connected to its owner expect in the case of identical twins. Its

nontransferable. The system can then compare scans to records stored in a

central or local database or even on a smart card.

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CONTENTS

Sr. No. Topics Page No.

1. Introduction to A.N.N. ……………………………… 6

2. Resemblence with brain ……………………………... 7-8

3. Structure of neural network………………………... 9-10

4. Architecture of neural networks …………………... 11-12

5. Face recognition using A.N.N. ……………………... 13-16

6. Capturing of image by standard video cameras….. 17-19

7. Components of face recognition systems ……...….. 20-21

8. Performance……………………………………...….. 22

9. Implementation of face recognition technology….. .. 23-

25

10. How face recognition systems work …………...….. 26-27

11. The software ……………………………………...….. 28-29

12. Advantages and disadvantages of face recognition 30

13. Applications ……………………………………...….. 31

14. Conclusion ……………………………………...….. 32

15. Bibliography……………………………………...….. 33

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CHAPTER – 1.

INTRODUNCTION

A neural network is a powerful data modeling tool that is able to capture and represent

complex input/output relationships . In the broader sense, a neural network is a collection of

mathematical models that emulate some of the observed properties of biological nervous

systems and draw on the analogies of adaptive biological learning. It is composed of a large

number of highly interconnected processing elements that are analogous to neurons and are

tied together with weighted connections that are analogous to synapses.

To be more clear, let us study the model of a neural network with the help of figure.1.

The most common neural network model is the multilayer perceptron (MLP). It is composed

of hierarchical layers of neurons arranged so that information flows from the input layer to the

output layer of the network. The goal of this type of network is to create a model that correctly

maps the input to the output using historical data so that the model can then be used to produce

the output when the desired output is unknown.

Figure 1.1. Graphical representation of MLP

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CHAPTER – 2.

RESEMBLENCE WITH BRAIN

The brain is principally composed of about 10 billion neurons , each connected to

about 10,000 other neurons. Each neuron receives electrochemical inputs from other neurons

at the dendrites. If the sum of these electrical inputs is sufficiently powerful to activate the

neuron, it transmits an electrochemical signal along the axon, and passes this signal to the

other neurons whose dendrites are attached at any of the axon terminals. These attached

neurons may then fire.

So, our entire brain is composed of these interconnected electro-chemical

transmitting neurons. From a very large number of extremely simple processing units (each

performing a weighted sum of its inputs, and then firing a binary signal if the total input

exceeds a certain level) the brain manages to perform extremely complex tasks. This is the

model on which artificial neural networks are based.

Neural network is a sequence of neuron layers. A neuron is a building block of a neural

net. It is very loosely based on the brain's nerve cell. Neurons will receive inputs via weighted

links from other neurons. This inputs will be processed according to the neurons activation

function. Signals are then passed on to other neurons.

In a more practical way, neural networks are made up of interconnected processing

elements called units which are equivalent to the brains counterpart ,the neurons.

Neural network can be considered as an artificial system that could perform

"intelligent" tasks similar to those performed by the human brain. Neural networks resemble

the human brain in the following ways:

1. A neural network acquires knowledge through learning.

2. A neural network's knowledge is stored within inter-neuron connection strengths

known as synaptic weights.

3. Neural networks modify own topology just as neurons in the brain can die and new

synaptic connections grow.

Graphically let us compare a artificial neuron and a neuron of a brain with the help of figures

2.1 and 2.2 given below

7

]

Figure 2.1. Neuron of an artificial neural network

Figure2.2.Neuron of a brain

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CHAPTER – 3.

STRUCTURE OF NEURAL NETWORK

According to Frank Rosenblatt’s theory in 1958 ,the basic element of a neural network

is the perceptron, which in turn has 5 basic elements: an n-vector input, weights, summing

function, threshold device, and an output. Outputs are in the form of -1 and/or +1. The

threshold has a setting which governs the output based on the summation of input vectors. If

the summation falls below the threshold setting, a -1 is the output. If the summation exceeds

the threshold setting, +1 is the output. Figure 3.1 depicts the structure of a basic perceptron

which is also called artificial neuron.

Figure 3.1. Artificial Neuron ( Perceptron)

The perceptron can also be dealt as a mathematical model of a biological neuron.

While in actual neurons the dendrite receives electrical signals from the axons of other

neurons, in the perceptron these electrical signals are represented as numerical values.

A more technical investigation of a single neuron perceptron shows that it can have an

input vector X of N dimensions (as illustrated in figure.5). These inputs go through a vector W

of Weights of N dimension. Processed by the Summation Node, "a" is generated where "a" is

the "dot product" of vectors X and W plus a Bias. "A" is then processed through an activation

function which compares the value of "a" to a predefined Threshold. If "a" is below the

Threshold, the perceptron will not fire. If it is above the Threshold, the perceptron will fire one

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pulse whose amplitude is predefined.

Figure 3.2. Mathematical model of a perceptron

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CHAPTER – 4.

ARCHITECTURE OF NEURAL NETWORK

4.1.Feed-forward networks:-

Feed-forward ANNs allow signals to travel one way only; from input to output. There

is no feedback (loops) i.e. the output of any layer does not affect that same layer. Feed-

forward ANNs tend to be straight forward networks that associate inputs with outputs. They

are extensively used in pattern recognition. This type of organisation is also referred to as

bottom-up or top-down.

4.2.Feed-back networks:-

Feed-back networks can have signals travelling in both directions by introducing loops

in the network. Feedback networks are very powerful and can get extremely complicated.

Feedback networks are dynamic; their 'state' is changing continuously until they reach an

equilibrium point. They remain at the equilibrium point until the input changes and a new

equilibrium needs to be found. Feedback architectures are also referred to as interactive or

recurrent, although the latter term is often used to denote feedback connections in single-layer

organisations.

4.3.Network layers:-

The commonest type of artificial neural network consists of three groups, or

layers, of units: a layer of “input” units is connected to a layer of “ hidden” units , which

is connected to a layer of “output” units.

1.The activity of the input units represents the raw information that is fed into the

network.

2. The activity of each hidden unit is determined by the activities of the input units and

the weights on the connections between the input and the hidden units.

3. The behavior of the output units depends on the activity of the hidden units and the

weights between the hidden and output units.

This simple type of network is interesting because the hidden units are free to construct

their own representations of the input. The weights between the input and hidden units

determine when each hidden unit is active, and so by modifying these weights, a hidden unit

can choose what it represents.

We also distinguish single-layer and multi-layer architectures. The single-layer

organisation, in which all units are connected to one another, constitutes the most general case

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and is of more potential computational power than hierarchically structured multi-layer

organisations. In multi-layer networks, units are often numbered by layer, instead of following

a global numbering.

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CHAPTER – 5.

FACE RECOGNITION USING NEURAL NETWORKS

5.1. Introduction

Face recognition is becoming a very promising tool for automatic multimedia content

analysis and for a content based indexing video retrieval system. The video raw data is first

automatically segmented into shots and from the content-related image segments, salient

features such as region shape, intensity, color, texture and motion descriptors are extracted

and used for indexing and retrieving information. In order to allow queries at a higher

semantic level, Image pre-processing and normalization is significant part of face recognition

systems. Changes in lighting conditions produces dramatically decrease of recognition

performance. If an image is low contrast and dark, we wish to improve its contrast and

brightness. The widespread histogram equalization cannot correctly improve all parts of the

image. When the original image is irregularly illuminated, some details on resulting image

will remain too bright or too dark.

Three main tasks of face recognition may be named: “document control”, “access

control”, and “database retrieval”. The term “document control” means the verification of a

human by comparison his/her actual camera image with a document photo. Access control is

the most investigated task in the field. Such systems compare the portrait of a tested person

with photos of people who have access permissions to joint used object.

5.2.Biometrics

A biometric is a unique, measurable characteristic of a human being that can be

used to automatically recognize an individual or verify an

individual’s identity. Biometrics can measure both physiological and behavioral

characteristics. Physiological biometrics (based on measurements and data derived from

direct measurement of a part of the human body) include:

1. Finger-scan

2. Facial Recognition

3. Iris-scan

4. Retina-scan

5. Hand-scan

Behavioral biometrics (based on measurements and data derived from an action)

include:

1. Voice-scan

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2. Signature-scan

3. Keystroke-scan

A “biometric system” refers to the integrated hardware and software used to conduct

biometric identification or verification.

Why we choose face recognition over other biometric?

There are a number reasons to choose face recognition. This includes the

following :

1. It requires no physical inetraction on behalf of the user.

2. It is accurate and allows for high enrolment and verification rates.

3. It does not require an expert to interpret the comparison result.

4. It can use your existing hardware infrastructure, existing camaras and image

capture devices will work with no problems.

5. It is the only biometric that allow you to perform passive identification in a one to

many environment (eg: identifying a terrorist in a busy Airport terminal.

5.3. Face Recognition

5.3.1. THE FACE

The face is an important part of who you are and how people

identify you. Except in the case of identical twins, the face is arguably a

person's most unique physical characteristics. While humans have the innate

ability to recognize and distinguish different faces for millions of years , computers are

just now catching up.

For face recognition there are two types of comparisons .the first is verification. This

is where the system compares the given individual with who that individual says they are

and gives a yes or no decision. The second is identification. This is where the system

compares the given individual to all the

other individuals in the database and gives a ranked list of matches. All identification or

authentication technologies operate using the following four stages:

1. capture: a physical or behavioural sample is captured by the system during

enrollment and also in identification or verification process.

2. Extraction: unique data is extracted from the sample and a template is created.

3. Comparison: the template is then compared with a new sample.

4. Match/non match : the system decides if the features extracted from the new sample

are a match or a non match.

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Face recognition technology analyze the unique shape ,pattern and positioning of the

facial features. Face recognition is very complex technology and is largely software based.

This Biometric Methodology establishes the analysis framework with tailored algorithms

for each type of biometric device.

Face recognition starts with a picture, attempting to find a person in the image. This

can be accomplished using several methods including movement, skin tones, or blurred

human shapes. The face recognition system locates the head and finally the eyes of the

individual. A matrix is then developed based on the characteristics of the individual’s face.

The method of defining the matrix varies according to the algorithm (the mathematical

process used by the computer to perform the comparison). This matrix is then

compared to matrices that are in a database and a similarity score is generated for

each comparison.

Artificial intelligence is used to simulate human interpretation of faces. In order to

increase the accuracy and adaptability, some kind of machine learning has to be implemented.

There are essentially two methods of capture. One is video imaging and the other

is thermal imaging. Video imaging is more common as standard video cameras can be used.

The precise position and the angle of the head and the surrounding lighting conditions may

affect the system performance. The complete facial image is usually captured and a

number of points on the face can then be mapped, position of the eyes, mouth and the

nostrils as a example. More advanced technologies make 3-D map of the face which

multiplies the possible measurements that can be made.

Thermal imaging has better accuracy as it uses facial temperature variations caused

by vein structure as the distinguishing traits. As the heat pattern is emitted from the face

itself without source of external radiation these systems can capture images despite the

lighting condition, even in the dark. The drawback is high cost. They are more expensive than

standard video cameras.

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Block Diagram:

Table 5.1

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CHAPTER – 6.

CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS

The image is optical in characteristics and may be thought of as a collection of a large

number of bright and dark areas representing the picture details. At an instant there will

be large number of picture details existing simultaneously each representing the level of

brightness of the scene to be reproduced. In other words the picture information is a

function of two variables: time and space. Therefore it would require infinite number

of channels to transmit optical information corresponding to picture elements

simultaneously. There are practical difficulty in transmitting all information

simultaneously so we use a method called scanning.

Here the conversion of optical information to electrical form and its transmission is

carried out element by element one at a time in a sequential manner to cover the entire image.

A TV camera converts optical information into electrical information, the amplitude of which

varies in accordance with variation of brightness.

An optical image of the scene to be transmitted is focused by lense assembly on the

rectangular glass plate of the camera tube. The inner side of this has a transparent coating

on which is laid a very thin layer of photoconductive material. The photolayer has

very high resistance when no light is falling on it but decreases depending on the intensity

of light falling on it. An electron beam is formed by an electron gun in the TV camera tube.

This beam is used to pick up the picture information now available on the target plate

of varying resistance at each point.

The electron beam is deflected by a pair of deflecting coils mounted on the glass

envelope and kept mutually perpendicular to each other to achieve scanning of the entire

target area. The deflecting coils are fed separately from two sweep oscillators, each

operating at different frequencies.

The magnetic deflection caused by current in one coil gives horizontal motion to the

beam from left to right at a uniform rate and brings it back to the left side to commence the

trace of the next line. The other coil is used to deflect the beam from top to bottom.

17

Figure 6.1

Figure 6.2

As the beam moves from element to element it encounters different resistance

across the target plate depending on the resistance of the photoconductive coating. The

result is flow of current which varies in magnitude as elements are scanned. The

current passes through the load resistance Rl connected to conductive coating on one

side of the DC supply source on the other. Depending on the magnitude of current a

varying voltage appears across the resistance Rl and this corresponds to the optical

18

information of the picture.

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CHAPTER – 7.

COMPONENTS OF FACE RECOGNITION SYSTEMS

An automated mechanism that scans and captures a digital or an analog image of a

living personal characteristics.(enrollment module) Another entity which handles

compression, processing, storage and compression of the captured data with stored data

(database) The third interfaces with the application system ( identification module).

Figure:8.1.

Figure:7.1.

User interface captures the analog or digital image of the person's face. In the

enrollment module the obtained sample is preprocessed and analyzed. This analyzed data

is stored in the database for the purpose of future comparison.

The database compresses the obtained sample and stores it. It should have

retrival property also that is it compares all the stored sample with the newly

obtained sample and retrieves the matched sample for the purpose of verification by the user

and determine whether the match declared is right or wrong.

The verification module also consists of a preprocessing system. Verification means

the system checks as to who the person says he or she is and gives a yes or no decision. In this

module the newly obtained sample is preprocessed and compared with the sample stored

20

in the database. The decision is taken depending on the match obtained from the

database. Correspondingly the sample is accepted or rejected.

Instead of verification module we can make use of identification module. In this the

sample is compared with all the other samples stored in the database. For each comparison

made a match score is given. The decision to accept or reject the sample depends on this

match score falling above or below a predetermined threshold.

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CHAPTER-8.

PERFORMANCE

8.1. False Acceptance Rate (FAR)

The probability that a system will incorrectly identify an individual or will fail to

reject an imposter. It is also called as type 2 error rate.

FAR= NFA/NIIA

Where FAR= false acceptance rate

NFA= number of false acceptance

NIIA= number of imposter identification attempts

8.2. False Rejection Rates (FRR)

The probability that a system will fail to identify an enrollee. It is also called type 1

error rate.

FRR= NFR/NEIA

Where FRR= false rejection rates

NFR= number of false rejection rates

NEIA= number of enrollee identification attempt

8.3. Response Time:

The time period required by a biometric system to return a decision on

identification of a sample.

8.4. Threshold/ Decision Threshold:

The acceptance or rejection of a data is dependent on the match score falling above or

below the threshold. The threshold is adjustable so that the system can be made more or less

strict; depending on the requirements of any given application.

8.5. Enrollment Time:

The time period a person must spend to have his/her facial reference template

successfully created.

8.6. Equal Error Rate:

When the decision threshold of a system is set so that the proportion of false

rejection will be approximately equal to the proportion of false acceptance. This synonym

is 'crossover rate'. The facial verification process involves computing the distance

between the stored pattern and the live sample. The decision to accept or reject is

dependent on a predetermined threshold. (Decision threshold).

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CHAPTER-9.

IMPLEMENTATION OF FACE RECOGNITION

TECHNOLOGY

The implementation of face recognition technology include the following four

stages:

1. Data acquisition

2. Input processing

3. Face image classification

4. Decision making

9.1. Data acquisition:

The input can be recorded video of the speaker or a still image. A sample of 1 sec

duration consists of a 25 frame video sequence. More than one camera can be used to produce

a 3D representation of the face and to protect against the usage of photographs to gain

unauthorized access.

9.2. Input processing:

A pre-processing module locates the eye position and takes care of the surrounding

lighting condition and colour variance. First the presence of faces or face in a scene must

be detected. Once the face is detected, it must be localized and normalization process may be

required to bring the dimensions of the live facial sample in alignment with the one on the

template.

Some facial recognition approaches use the whole face while others concentrate

on facial components and/ or regions(such as lips, eyes etc). the appearance of the face can

change considerably during speech and due to facial expressions. In particular the mouth is

subjected to fundemental changes but is also very important source for discriminating faces.

So an approach to persons recognition is developed based on spatio-temporal modeling of

features extracted from talking face. Models are trained specific to a persons speech

articulate and the way that the person speaks. Person identification is performed by

tracking mouth movements of the talking face and by estimating the likelyhood of each

model of having generated the observed sequence of features. The model with the

highest likelyhood is chosen as the recognized person.

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Block diagram:

Figure 9.2.1 Input processing

9.3.Face image classification and decision making:

Figure 9.3.Face image classification and decision making

Synergetic computer are used to classify optical and audio features, respectively.

A synergetic computer is a set of algorithm that features, respectively. A synergetic

computer is a set of algorithm that simulate synergetic phenomena. In training phase

the BIOID creates a prototype called faceprint for each person. A newly recorded

pattern is preprocessed and compared with each faceprint stored in the database. As

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comparisons are made, the system assigns a value to the comparison using a scale of one

to ten. If a score is above a predetermined threshold, a match is declared.

From the image of the face, a particular trait is extracted. It may measure various

nodal points of the face like the distance between the eyes ,width of nose etc. it is

fed to a synergetic computer which consists of algorithm to capture, process, compare the

sample with the one stored in the database. We can also track the lip movements

which is also fed to the synergetic computer. Observing the likely hood each of the

sample with the one stored in the database we can accept or reject the sample.

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CHAPTER-10.

HOW FACE RECOGNITION SYSTEMS WORK

10.1.An example:

Visionics, company based in a New Jersey is one of the many developers of

facial recognition technology. The twist to its particular software, Face it is that it can

pick someone's face from the rest of the scene and compare it to a database full of stored

images . In order for this software to work, it has to know what a basic face looks like. Facial

recognition software is based on the ability to first recognize faces, which is a

technological feat in itself and then measure the various features of each face.

Figure : 10.1

If you look at the mirror, you can see that your face has certain distinguishable

landmarks. These are the peaks and valleys that make up the different facial features.

Visionics defines these landmarks as nodal points.

There are about 80 nodal points on a human face. Here are few nodal points that are

measured by the software.

1. Distance between the eyes

2. Width of the nose

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3. Depth of the eye socket

4. Cheekbones

5. Jaw line

6. Chin

These nodal points are measured to create a numerical code, a string of

numbers that represents a face in the database. This code is called face print. Only 14 to

22 nodal points are needed for facial software to complete the recognition process.

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CHAPTER-11.

THE SOFTWARE

Facial recognition software falls into a larger group of technologies known as

biometrics. Facial recognition methods may vary, but they generally involve a series of

steps that serve to capture, analyze and compare your face to a database of stored

images. Here is the basic process that is used by the Faceit system to capture and compare

images:

11.1.Detection

When the system is attached to a video surveillance system, the recognition

software searches the field of view of a video camera for faces. If there is a face in the view, it

is detected within a fraction of a second. A multi- scale algorithm is used to search for faces in

low resolution. (An algorithm is a program that provides a set of instructions to accomplish a

specific task). The system switches to a high-resolution search only after a head-like

shape is

detected.

11.2. Alignment

Once a face is detected, the system determines the head's position, size and pose. A

face needs to be turned at least 35 degrees toward the camera for the system to register it.

11.3. Normalization

The image of the head is scaled and rotated so that it can be registered and

mapped into an appropriate size and pose. Normalization is performed regardless of the

head's location and distance from the camera. Light does not impact the normalization

process.

11.4. Representation

The system translates the facial data into a unique code. This coding process

allows for easier comparison of the newly acquired facial data to stored facial data.

11.5. Matching

The newly acquired facial data is compared to the stored data and (ideally) linked to

at least one stored facial representation. The heart of the Faceit facial recognition

system is the Local Feature Analysis (LFA) algorithm. This is the mathematical technique

the system uses to encode faces.

28

The system maps the face and creates a face print, a unique numerical code for that

face. Once the system has stored a face print, it can compare it to the thousands or

millions of face prints stored in a database. Each face print is stored as an 84-byte file.

Using facial recognition software, police can zoom in with cameras and take a snapshot of a

face.

Figure : 11.5.1. MATCHING OF FACES

The system can match multiple face sprints at a rate of 60 million per minute from

memory or 15 million per minute from hard disk. As comparisons are made, the system

assigns a value to the comparison using a scale of one to 10. If a score is above a

predetermined threshold, a match is declared. The operator then views the two photos

that have been declared a match to be certain that the computer is accurate.

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CHAPTER-12.

ADVANTAGES AND DISADVANTAGES OF FACE

RECOGNITION USING NEURAL NETWORK

12.1.Advantages:

1.There are many benefits to face recognition systems such as its convinince

and social acceptability. All you need is your picture taken for it to work.

2. Face recognition is easy to use and in many cases it can be performed without a

person even knowing.

3. Face recognition is also one of the most inexpensive biometric in the market and its

prices should continue to go down.

12.2.Disadvantages:

1. Face recognition systems cant tell the difference between identical twins.

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CHAPTER-13.

APPLICATIONS

The natural use of face recognition technology is the replacement of PIN, physical

tokens or both needed in automatic authorization or identification schemes. Additional

uses are automation of human identification or role authentication in such cases where

assistance of another human needed in verifying the ID cards and its beholder.

There are numerous applications for face recognition technology:

Government Use

1. Law Enforcement: Minimizing victim trauma by narrowing mugshot searches,

verifying identify for court records, and comparing school surveillance camera

images to known child molesters.

2. Security/Counterterrorism. Access control, comparing surveillance images to

known terrorists.

3. Immigration: Rapid progression through Customs.

Commercial Use

1. Day Care: Verify identity of individuals picking up the children.

2. Residential Security: Alert homeowners of approaching personnel.

3. Voter verification: Where eligible politicians are required to verify their identity

during a voting process. this is intended to stop 'proxy' voting where the vote

may not go as expected.

4. Banking using ATM: The software is able to quickly verify a customers face .

5. Physical access control of buildings areas ,doors, cars or net access.

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CHAPTER-14.

CONCLUSION

Face recognition technologies have been associated generally with very costly top

secure applications. Today the core technologies have evolved and the cost of equipments is

going down dramatically due to the intergration and the increasing processing power.

Certain application of face recognition technology are now cost effective, reliable and

highly accurate. As a result there are no technological or financial barriers for stepping

from the pilot project to widespread deployment.

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CHAPTER-15.

BIBLIOGRAPHY

1. ELECTRONICS FOR YOU- Part 1 April 2001 & Part 2 May 2001

2. ELECTRONIC WORLD - DECEMBER 2002

3. MODERN TELEVISION ENGINEERING- Gulati R.R

4. IEEE IN TELLIGENT SYS TEMS - MAY/JUNE 2003

5. WWW.FACEREG.COM

6. WWW. IMAGESTECHNOLOGY.COM

7. WWW.IEEE.COM

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