FACE RECOGINITION SYSTEM.docx

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ATechnical Seminar Report

On

“FACE RECOGINITION SYSTEM”

Submitted towards partial fulfilment of the requirement for the award of Degree of

Bachelor of Technology

In

Information Technology

Submitted to Submitted byVijendra Singh ame! "ansi Ahuja

#$oordinator% &r'o!()(*++

,ranch! IT

Moy In!tit"te of Technology # Science$ %a&!hmangarh

Fac"lty of Engineering # Technology

%a&!hmangarh'(()(**

A+ril$ ),*)



AC-NO.%E/GEMENT

I am e-tremely grateful to the Dean. /rof' Dr' /'0 Das. for the guidance and encouragement and

for pro1iding me with best facilities and atmosphere for the creati1e wor2'

I would li2e to than2 3ead of Department #$S&%. /rof' A'0 Sharma for the 1aluable guidance.

care and timely support throughout the seminar wor2' 3e has always a constant source of

encouragement'

I would li2e to than2 the Seminar $oordinator "r'Vijendra Singh. for his 1aluable suggestions

towards the preparation of my seminar'

I would li2e to than2 my friends and family for their encouragement. which helped me to 2eep

my spirit ali1e and to complete this wor2 successfully'

ABSTRACT



Ta0le of Content!



• Introduction

• 4ace Recognition System

• The face as a biometric feature

o Definition of ,iometric

o &-ample of ,iometric

• Techniques used for face recognition system

o Traditional technique

o 5D technique

o S2in te-ture analysis

• 3ow the system wor2s6

o Detection

o Alignment

o "easurement

o Representation

o "atching

o Verification 7 Identification• Technologies 8sed 4or 4ace Recognition

o &igen faces

o 4eature analysis

o eural networ2

o Automatic face processing

• Implementation of 4ace Recognition Technology

• Ad1antages and Disad1antages

• 9hat ma2es face recognition so difficult6

• Applications

• $onclusion• References

INTRO/1CTION



: /eople ha1e ama;ing ability to recogni;e and remember thousands of faces'

: 4ace is an important part of who you are and how people identify you'

: 9hile humans ha1e had the innate ability to recogni;e and distinguish faces for millions of

years. computers are just catching up'

: 4ace recognition is a fascinating problem with important commercial applications such as mug

shot matching. crowd sur1eillance 7 witness face reconstruction'

: In computer 1ision most of the popular face recognition algorithms ha1e been biologically

moti1ated'

: 8sing these models researchers can quantify the similarity between faces. images whose

projections are close in face space are li2ely to be from the same indi1idual'

: $ompare results of these models with human perception to determine whether distance in face

space corresponds to the human notion of facial similarity'

: ,iometrics is used for that purpose'

FACE RECOGINITION SYSTEM



3uman face recognition has drawn considerable attention from the researchers in recent years'

An automatic face recognition system will find many applications in areas such as human<

computer interfaces. model<based 1ideo coding and security control systems'

In addition. face recognition has the potential of being a non<intrusi1e form of biometric

identification'

The difficulties of face recognition lie in the inherent 1ariability arising from face characteristics

#age. gender and race%. geometry #distance and 1iewpoint%. image quality #resolution.

illumination. signal to noise ratio%. and image content #bac2ground. occlusion and disguise%'

,ecause of such comple-ity. most face recognition systems to date assume a well<controlled

en1ironment and recogni;e only near frontal faces' 3owe1er. these constraints need to be

rela-ed in practice'

Also. in applications such as 1ideo database search. a person=s face can appear in arbitrary

bac2grounds with un2nown si;e and orientation'

Thus there is a need for robust face recognition systems to handle these uncertainties'

.hat i! 0iometric2



A biometric is a unique. measurable characteristic of a human being that can be used to

automatically recogni;e an indi1idual or 1erify an indi1idual identity' ,iometrics can measure

both physiological and beha1ioral characteristics'

/efinition of Biometric!

>Any automatically mea!"ra0le. ro0"!t and i!tincti3e physical characteristic or +er!onal trait

that can be used to identify an indi1idual or 1erify the claimed identity of an indi1idual?

This definition requires elaboration!<

Mea!"ra0le means that the characteristic or trait can be easily presented to a sensor. located

by it. and con1erted into a quantifiable. digital format' This measurability allows for matching

to occur in a matter of seconds and ma2es it an automated process'

Ro0"!tne!! of a biometric refers to the e-tent to which the characteristic or trait is subject

to significant changes o1er time' A highly robust biometric does not change significantly

o1er time while a less robust biometric will change' 4or e-ample. the iris. which changes

1ery little o1er a person=s lifetime. is more robust than one=s 1oice'

/i!tincti3ene!! is a measure of the 1ariations or differences in the biometric pattern among

the general population' The higher the degree of distincti1eness. the more indi1idual is

the identifier' A low degree of distincti1eness indicates a biometric pattern found frequently

in the general population' The iris and the retina ha1e higher degrees of distincti1eness than

hand or finger geometry'

E4am+le! of Biometric

Iris scan

Retinal scan

4ace recognition

Spea2er@Voice

4ingerprint

3and@4inger geometry



Signature 1erification

Iri! Scan

Iris scanning measures the iris pattern in the colored part of the eye. although the iris

color has nothing to do with the biometric' Iris patterns are formed randomly' As a result.

the iris patterns in a person=s left and right eyes are different. and so are the iris patterns of

identical twins' Iris scanning can be used quic2ly for both identification and 1erification

applications because the iris is highly distincti1e and robust'

Retinal Scan

Retinal scans measure the blood 1essel patterns in the bac2 of the eye' The de1ice in1ol1es

a light source shined into the eye of a user who must be standing 1ery still within inches of

the de1ice' ,ecause users percei1e the technology to be somewhat intrusi1e. retinal scanning

has not gained popularity currently retinal scanning de1ices are not commercially a1ailable'

Facial Recognition

4acial recognition records the spatial geometry of distinguishing features of the face'

Different 1endors use different methods of facial recognition. howe1er. all focus on measures

of 2ey features of the face' ,ecause a person=s face can be captured by a camera from some

distance away. facial recognition has a clandestine or co1ert capability # i.e. the subject does not

necessarily 2now he has been obser1ed%' 4or this reason. facial recognition has been used in

projects to identify card counters or other undesirables in casinos. shoplifters in stores. criminals

and terrorists in urban areas'

S+ea&er 5 6oice Recognition

Voice or spea2er recognition uses 1ocal characteristics to identify indi1iduals using a pass<

phrase' A telephone or microphone can ser1e as a sensor. which ma2es it a relati1ely cheap and



easily deployable technology' 3owe1er. 1oice recognition can be affected by en1ironmental

factors such as bac2ground noise' This technology has been the focus of considerable efforts on

the part of the telecommunications industry and the 8'S' go1ernment=s intelligence community.

which continue to wor2 on impro1ing reliability'

Finger+rint

The fingerprint biometric is an automated digital 1ersion of the old in2and< paper method used

for more than a century for identification. primarily by law enforcement agencies' The biometric

de1ice in1ol1es users placing their finger on a platen for the print to be electronically read' The

minutiae are then e-tracted by the 1endor=s algorithm. which also ma2es a fingerprint pattern

analysis' 4ingerprint biometrics currently ha1e three main application arenas! large<scale

Automated 4inger Imaging Systems #A4IS% generally used for law enforcement purposes. fraud

pre1ention in entitlement programs. and physical and computer access'

7an5Finger Geometry

3and or finger geometry is an automated measurement of many dimensions of the hand and

fingers' either of these methods ta2es actual prints of the palm or fingers' Spatial geometry is

e-amined as the user puts his hand on the sensor=s surface and uses guiding poles between the

fingers to properly place the hand and initiate the reading' 4inger geometry usually measures two

or three fingers' 3and geometry is a well<de1eloped technology that has been thoroughly field<

tested and is easily accepted by users' ,ecause hand and finger geometry ha1e a low degree of

distincti1eness. the technology is not well<suited for identification applications'

/ynamic Signat"re 6erification



9e ha1e long used a written signature as a means to ac2nowledge our identity' Dynamic

signature 1erification is an automated method of measuring an indi1idual=s signature' This

technology e-amines such dynamics as speed. direction. and pressure of writing the time that

the stylus is in and out of contact with the >paper.? the total time ta2en to ma2e the signature

and where the stylus is raised from and lowered onto the >paper'?

-ey!tro&e /ynamic!

0eystro2e dynamics is an automated method of e-amining an indi1idual=s 2eystro2es on a

2eyboard' This technology e-amines such dynamics as speed and pressure. the total time ta2en to

type particular words. and the time elapsed between hitting certain 2eys' This technology=s

algorithms are still being de1eloped to impro1e robustness and distincti1eness' One potentially

useful application that may emerge is computer access. where this biometric could be

used to 1erify the computer user=s identity continuously'

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

ientification an 3erification'

9ith ientification. the biometric system as2s and attempts to answer the question.

>9ho is B6? In an identification application. the biometric de1ice reads a sample and

compares that sample against e1ery record or template in the database' This type of

comparison is called a >one<to<many? search #C!%' Depending on how the system is

designed. it can ma2e a >best? match. or it can score possible matches. ran2ing them in

order of li2elihood' Identification applications are common when the goal is to identify

criminals. terrorists. or other >wol1es in sheep=s clothing.? particularly through sur1eillance'

6erification occurs when the biometric system as2s and attempts to answer the question.



>Is this B6? after the user claims to be B' In a 1erification application. the biometric system

requires input from the user. at which time the user claims his identity 1ia a password.

to2en. or user name #or any combination of the three%' This user input points the system to a

template in the database' The system also requires a biometric sample from the user'

It then compares the sample to or against the user<defined template' This is called a

>one<to<one?search #C!C%' The system will either find or fail to find a match between the two'

Verification is commonly used for physical or computer access'

.hy choo!e face recognition o3er other 0iometric!2

C' It is non intrusi1e and requires no physical interaction on behalf of the user'

' It is accurate and allows for high enrollment and 1erification rates'

5' It does not require an e-pert to interpret the comparisons'

+' It can use your e-isting hardware infrastructure. e-isting cameras and image capture de1ices

will wor2 with no problem'

E' Fou can use e-isting images without ha1ing to re<enroll e1ery user'#e'g'! passports. id cards.

dri1ers licenses etc''%

*' It is the only biometric that allows performing passi1e identification in C to many

en1ironments #eg! identifying a terrorist in a busy airport terminal%'

G' In some cases one may also want to loo2 at deploying multilayered biometric '&-ample of

multi layered biometric would be the use of both finger print scans and facial scans in one

unified access control de1ices'

TEC7NI81ES 1SE/ FOR FACE RECOGINITION9'



*: TRA/ITIONA% TEC7NNI81E

Some facial recognition algorithms identify faces by e-tracting landmar2s. or features. from an

image of the subjectHs face' 4or e-ample. an algorithm may analy;e the relati1e position. si;e.

and@or shape of the eyes. nose. chee2bones. and jaw' These features are then used to search for

other images with matching features' Other algorithms normali;e a gallery of face images and

then compress the face data. only sa1ing the data in the image that is useful for face detection' A

probe image is then compared with the face data'

4ace distinguishable lanmar&! &no;n a! NO/A% <OINTS

3uman face has app' )( nodal points such as!<

Distance between the eyes 9idth of the nose

Depth of the eye soc2ets

The shape of the chee2bones

The length of the jaw line

These nodal points are measured creating a numeric code. called a FACE <RINT

Traitional Techni="e

): ('/ TEC7NI81E



Three'imen!ional face recognition #5D face recognition% is a modality of facial

recognition methods in which the three<dimensional geometry of the human face is used' It has

been shown that 5D face recognition methods can achie1e significantly higher accuracy than

their D counterparts. ri1aling fingerprint recognition'

5D face recognition has the potential to achie1e better accuracy than its D counterpart by

measuring geometry of rigid features on the face' This a1oids such pitfalls of D face

recognition algorithm as change in lighting. different facial e-pressions. ma2e<up and head

orientation' Another approach is to use the 5D model to impro1e accuracy of traditional image

based recognition by transforming the head into a 2nown 1iew' Additionally. most range

scanners acquire both a 5D mesh and the corresponding te-ture' This allows combining the

output of pure 5D matchers with the more traditional D face recognition algorithms. thusyielding better performance'

The main technological limitation of 5D face recognition methods is the acquisition of 5D

images. which usually requires a range camera' Alternati1ely. multiple images from different

angles from a common camera may be used to create the 5D model with significant post<

processing' Recently commercial solutions ha1e implemented depth perception by projecting a

grid onto the face and integrating 1ideo capture of it into a high resolution 5D model' This allows

for good recognition accuracy with low cost off<the<shelf components'

A/6ANTAGES 9'

It is not affected by changes in lighting'

Identify a face from a range of 1iewing angles'

('/ Techni="e



S-IN TE>T1RE ANA%YSIS

8ses the 1isual details of the s2in'

Turns the unique lines. patterns. and spots apparent in a person=s s2in into a mathematical

space'

/erformance in recogni;ing faces increase up to ( to E '

S&in Te4t"re Analy!i!

7 O. FACE RECOGINITION SYSTEM .OR-S2



4acial recognition software falls into a larger group of technologies 2nown as biometrics' 4acial

recognition methods may 1ary. but they generally in1ol1e a series of steps that ser1e to capture.

analy;e and compare your face to a database of stored images' 3ere is the basic process that is

used by the 4ace it system to capture and compare images!

The facial recognition is SIB ST&/ /RO$&SS!<

STE< *: /ETECTION

9hen the system is attached to a 1ideo sur1eillance system. the recognition software searches

the field of 1iew of a 1ideo camera for faces' If there is a face in the 1iew. 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 pro1ides a set of instructions to accomplish a specific tas2%' The

system switches to a high<resolution search only after a head<li2e shape is detected'

A$J8IRIK T3& I"AK& O4 A IDIVID8AL=S 4A$&

9AFS TO AJ8IR& I"AK&!<

C% Digitally scan an e-isting photograph #D image%

% Acquire a li1e picture of a subject #5D image%

STE< ): A%IGNMNET



Once a face is detected. the system determines the headHs position. si;e and pose' A face needs to

be turned at least 5E degrees toward the camera for the system to register'

LO$AT& I"AK& O4 4A$&

Software is used to locate the faces in the image that has been obtained

Once it detects a face. the system determines the headHs position. si;e and pose

STE< (: MEAS1REMENT9'

AALFSIS O4 4A$IAL I"AK&

software measures face according to is pea2s and 1alleys #nodal points%

focuses on the inner region of the face 2nown as the >golden triangle?

nodal points are used to ma2e a face print

STE< ?: RE<RESENTATION



The system translates the facial data into a unique code also called as template' This coding

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

template is much smaller than the image from which it is drawn whereas quality facial images

generally require CE(<5(( 2b. the templates are appro-' C5(( bytes or less than C@C((th of

original'

The system translates the template into a unique code

This coding gi1es each template a set of numbers to represent the features on a

subjectHs face'

STE< @: MATC7ING

The newly acquired facial data is compared to the stored data and #ideally% lin2ed to atleast one

stored facial representation' The degree of similarity required for 1erification also 2nown as

threshold can be adjusted for different personnel=s. pcs. time of the day and other factors'



$O"/ARISO

the face print created by the software is compared to all face prints the system has stored

in its database'

STE< :6ERIFICATION # I/ENTIFICATION

"AT$3 OR O "AT$3

software decides whether or not any comparisons from step E are

close enough to declare a possible match

Technologie! 1!e For Face Recognition



: There are 1arious methods by which facial scan technology recogni;es people'

: All share certain commonalities such as emphasi;ing those sections of the face. which are less

susceptible to alteration. including the upper outlines of the eye soc2ets areas surrounding ones

chee2 bones and side of mouth'

: "ost technologies are resistant to moderate changes in hairstyle. as they do not utili;e areas of

the face located near hairline'

: All primary technologies are designed to be robust enough to conduct one to many searches i'e'

to locate single face out of a database of thousands of faces'

: There are ? +rimary metho! employed for facial scanning to identify and recogni;e and

1erify subjects'

C' &igen faces

' 4eature analysis

5' eural networ2

+' Automatic face processing'

*: Eigen face

: &igen face roughly translated. as >one=s own face? is a technology which utili;es d global

grayscale images representing distincti1e characteristics of a facial image'

: Variations of &igen faces are frequently used as the basis of other face recognition methods'

: As suggested by graphic. distincti1e characteristics of the entire face are highlighted for use in

future authentication'

: The 1ast majority of faces can be reconstructed by combining features of appro-imately C((<

CE &igen faces'

: 8pon enrollment. the subjects &igen face is mapped to a series of numbers #coefficients%'



: 4or one to one authentication. in which the image is being used to 1erify a claimed identity

ones >li1e? template is compared against the enrolled template to determine coefficient 1ariation'

: The degree of 1ariance from template will determine acceptance or rejection'

: 4or one to many identification. the same principle applies but with a much larger comparison

set'

: Li2e all facial recognition technology. &igen face technology is best utili;ed in well<lit. frontal

image capture situation'

): Feat"re Analy!i!

: 4eature analysis is the most widely used facial recognition technology'

: It is related to &igen face but is more capable of accommodating changes in appearance or

facial aspect #smiling 1s' frowning%'

: It uses L4A #local feature analysis%. which can be summari;ed as an >irreducible set of building

elements?'



: L4A utili;es do;ens of features from different regions of the face and also incorporates the

relati1e location of the features'

: The e-tracted #1ery small% features are building bloc2s and both types of bloc2s and their

arrangement are used to identify or 1erify'

: It anticipates that the slight mo1ement of feature located near ones mouth will be accompanied

by relati1ely similar mo1ement of adjacent features'

&'K' if a person is smiling then the changes that occur in the features adjacent to mouth are also

ta2en into consideration'

: Since feature analysis is not global representation of the face. it can accommodate angle up to

appro-imately E degree in hori;ontal plane. appro-imately CE degree in 1ertical plane'

: Of course a straightforward 1ideo image from a distance of 5 feet will be the most accurate'

: 4eature analysis is robust enough to perform one to one or one to many searches'

(: Ne"ral Net;or& Ma++ing Technology

: In this technology features from both the faces i'e' the enrollment and 1erification face<1ote on

whether there is a match'

: eural networ2s employ an algorithm to determine the similarity of unique global features of

li1e 1s' enrolled or reference faces using as much of facial images as possible'

: An incorrect match #false match% prompts the matching algorithm to modify the weight it gi1es

to certain facial features'

: This method theoretically leads to an increased ability to identify faces in difficult conditions'

: As with all primary technologies neural networ2 facial recognition can do one to one or one to

many'

?: A"tomatic Face <roce!!ing



: It is more rudimentary technology using distances and distance ratios between easily acquired

features such as eyes. end of nose and corners of mouth'

: Though o1erall not as robust as &igen face. feature analysis or neural networ2 automatic face

processing may be more effecti1e in dimly lit. frontal image capture situations'

: Also it uses the combined approach of abo1e three techniques'

IM<%EMENTATION OF FACE RECOGNITION TEC7NO%OGY

The implementation of face recognition technology includes the following four stages!

:Data acquisition

:Input processing

: 4ace image classification and decision ma2ing



/ata ac="i!ition9

The input can be recorded 1ideo of the spea2er or a still image' A sample of C sec durationconsists of a E frame 1ideo sequence' "ore than one camera can be used to produce a 5D

representation of the face and to protect against the usage of photographs to gain unauthori;ed

access'

In+"t +roce!!ing9

A pre<processing module locates the eye position and ta2es care of the surrounding lightingcondition and color 1ariance' 4irst the presence of faces or face in a scene must be detected'

Once the face is detected. it must be locali;ed and ormali;ation process may be required to

bring the dimensions of the li1e 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 e-pressions' In particular the mouth is subjected to fundamental

changes but is also 1ery important source for discriminating faces'

FACE IMAGE C%ASSIFICATION

"odels are trained specific to a person speech articulate and the way that the person spea2s'

/erson identification is performed by trac2ing mouth mo1ements of the tal2ing face and by

estimating the li2elihood of each model of ha1ing generated the obser1ed sequence of features'

The model with the highest li2elihood is chosen as the recogni;ed person' Synergetic computer

are used to classify optical and audio features. respecti1ely' A synergetic computer is a set of

algorithm that simulates synergetic phenomena' In training phase the ,IOID creates a prototype

called face print for each person'



/ECISION MA-ING

A newly recorded pattern is preprocessed and compared with each face print stored in the

database' As comparisons are made. the system assigns a 1alue to the comparison using a scale

of one to ten' If a score is abo1e a predetermined threshold. a match is declared' '

4rom the image of the face. a particular trait is e-tracted' It may measure 1arious nodal points of

the face li2e 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' 9e can also trac2 the lip mo1ement which is also fed to the synergetic computer'

Obser1ing the li2elihood each of the samples with the one stored in the database we can accept

or reject the sample'

A/ 6ANTAGES AN/ /ISA/6ANTAGES

A3antage!9

a' There are many benefits to face recognition systems such as its con1enience and

Social acceptability' All you need is your picture ta2en for it to wor2'

b' 4ace recognition is easy to use and in many cases it can be performed without a



/erson e1en 2nowing'

c' 4ace recognition is also one of the most ine-pensi1e biometric in the mar2et and

Its price should continue to go down'

Face recognition carrie! ifferent ta!&!9'

4ace 1erification

4ace identification &-pression and emotion recognition

Age analysis

Lip reading

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FACE RECOGINITION SYSTEM.docx