23

CHAPTER 2

LITERATURE REVIEW

2.1 BIOMETRIC SYSTEM

Biometrics’ ability of automatic methods for identifying a person

on the basis of some biological or behavioural characteristic of the person is

an essential need in the personal device usage in public domain. As

fingerprint, face is also one of the most acceptable biometrics because it is a

common method of identification that human use in their visual interactions

and acquisition of faces. The biological facial geometric characteristics and

behavioural characteristics such as expressional variants are distinctive to

each person. Biometrics is related to identity-confirmation and security

techniques that rely on measurable, individual biological characteristics. For

example, facial patterns may be used to enable access to a computer, to a

room or to an electronic commerce account.

Biometrics refers to the identification of a person based on his or

her physiological or behavioural characteristics. Today there are many

biometric devices based on characteristics that are unique for everyone. Some

of these characteristics include, but are not limited to, fingerprints, hand

geometry, and voice (Varchol and Levicky 2007). These characteristics can

be used to positively identify someone. Many biometric devices are based on

the capture and matching of biometric characteristics in order to produce a

positive identification.

24

Every biometric system of devices includes three processes; they

are enrollment, live presentation and matching. The time of enrollment is

when the user introduces his or her biometric information to the biometric

device for the first time. The enrollment data is processed to form the stored

biometric template. Later, during the live presentation the user’s biometric

information is extracted by the biometric device and processed to form the

live biometric template. Finally, the stored biometric template and the live

biometric template are compared to one another at the time of matching to

provide the biometric score or result. There are various biometric techniques

available such as Finger print, face, iris, retina, handwriting, voice recognition

on an individual basis (Woodward et al. 2003).

The generally biometric system works to be the security application

commonly dividing for eight stages of the processing (Liu and Silverman

2000). First one is capture the chosen biometric, 2. Process the biometric and

extract and enroll the biometric template, 3. Store the template in a local

repository, a central repository or a portable token such as a smart card,

4. Live scan the chosen biometric, 5. Process the biometric and extract the

biometric template, 6. Match the scanned biometric template against stored

template, 7. Provide a matching score to business applications, 8. Record a

secure audit trail with respect to system use database, those the processing as

shown in Figure 2.1.

Figure 2.1 Generally Biometric System Works

25

Individuals who change their appearances markedly, by growing

beards, unusual facial expressions can confuse the system. Face recognition is

one of the concerns in unattended authentication applications. It is very

challenging to develop face recognition techniques which can tolerate the

effects of facial expressions, slight variations in the imaging environment and

variations in the pose of face with respect to camera. The orientation of a

person’s face toward the camera also can influence accuracy. These systems

capabilities are investigated in the constructed facial expressional system on

its deployment (Yacoob and Davis 1996). The biometrics’ factors reliability

and capability in distinguishing between a specific individual and an impostor

based on an identification document or a password are investigated in this

research.

One of the biggest challenges that the society faces today is

confirming the true identity of a person. There are several identity verification

schemes existing today but the most accurate identification schemes are only

possible with the area of biometrics. Some examples of identifying biometric

characteristics are fingerprints, hand geometry, retina and iris patterns, facial

geometry, signatures and voice recognition. Biometric identification may be

preferred over traditional methods (such that passwords, smart cards) because

its information is virtually impossible to steal. Although in some cases it may

become possible to impersonate a biometric (such that replicating legitimate

users fingerprints to fool the fingerprint scanning device). The interesting

property biometric identification is the person to be identified based on

biometric techniques does not depend on the method of the user to remember

a password or carry a token (Uludag et al. 2004).

2.2 TYPES OF BIOMETRIC CHARACTERISTICS

Biometrics is the use of physiological or behavioural characteristics

to determine or verify an individual’s identity. Looking at the nature of the

26

underlying modalities, two basic categories can be identified as Behavioural

(or active) and Physiological (or passive) features. Acquisition of biometric

information in the first category requires users to be active, that is to perform

some activity in front of a sensor, whereas data acquisition in biometric

systems of the second category, a bodily measurement is taken from subjects,

which does not necessarily require an action by the user. From the user’s

point of view, it can be stated that in the first category some co-operation is

required, whereas biometrics of the second category can be acquired even

without explicit consent of subjects (Jain et al. 2004).

With respect to potential applications, the differentiation between

behavioural and physiological biometrics can be of great importance for many

reasons. Among this variety, three aspects shall be mentioned to demonstrate

the differences in suitability of single biometric modalities.

Declaration of Intention: In scenarios, where user authentication

is linked to an explicit consent to the authentication process, behavioural

schemes appear more adequate than physiological. For example signature

verification constitutes a socially well-accepted and widely used process and

has been in application for many centuries. Besides the possibility for a

(manual) user authentication based on the visible and physical traces of the

writing process, signatures also serve for at least two additional goals:

Declaration of Intention and Warning functions. The first aspect

(Authentication) can be confirmed to due to the fact that the result of the

signature process represents individual properties of the writing style, intrinsic

to the writer. For the second aspect (Declaration of Intention), it can be

assumed that if the signature is linked to some document, the signer has

produced the signature in an agreeable attitude. The third function (Warning)

assumes that subjects are aware that signing documents can have severe

consequences and thus should be well considered (Delac and Grgic 2004).

27

Apparently, behavioural biometrics, particularly signature

verification as sub-discipline of handwriting biometrics, is more adequate to

reproduce these functions than physiological modalities. This particularly is

the case in environments, which are not continuously observed by trusted

persons, where no witnesses of voluntariness exist. It is necessary to be

mentioned however that behavioural method has tendency towards higher

error rates as compared to physiological biometrics.

Identification: Biometric authentication can be achieved in two

different modes i.e., verification and identification. Applications, where the

automated identification of persons is intended have quite different demands.

While behavioural features can easily be repudiated (such that disguise of a

particular writing style), this is not the case for physiological features. For

example in crime prevention, biometric recognition and automated search of

suspects can support observation of public areas. Obviously in this scenario,

disguise of biometric features is undesired and consequently, physiological

traits such as face recognition appear more practical.

Ascertainability: Another important criterion for the use of

particular biometric modalities is ascertainability, that is, the question, if the

biometric information can be acquired under different operational,

environmental and geographical conditions in sufficient quality and

quantities. For example, it appears difficult to implement speaker recognition

in scenarios such as factory halls, where noisy machinery is in use (Ciota

2004). On the other hand, signature verification used for access control to

buildings appears infeasible, when biometrics is to be verified frequently and

at numerous locations to and inside a building. Further, the later modality is

not appropriate, if it can be foreseen that subjects will not be able to use their

hands while transiting access control gates (Hao et al. 2006).

28

Another distinction between behavioural and physiological

biometrics is the possibility of including semantic information in behaviour.

A speaker for example can articulate a specific message in her or his

biometric trait as well as a writer in a handwriting trace. This characteristic

implies some advantages of behavioural biometrics, when combining them

with knowledge and possession-based authentication schemes (Dahel and

Xiao 2003).

2.2.1 Face Recognition

Facial images are the most common biometric characteristic used

by humans to make a personal recognition, hence the idea to use this

biometric in technology (Craw et al. 1999). This is a nonintrusive method and

is suitable for covert recognition applications. The applications of facial

recognition range from static to dynamic, uncontrolled face identification in a

cluttered background (subway, airport). Face verification involves extracting

a feature set from a two-dimensional image of the user’s face and matching it

with the template stored in a database. The most popular approaches to face

recognition are based on either: one is the location and shape of facial

attributes such as eyes, eyebrows, nose, lips and chin, and their spatial

relationships. Second is the overall (global) analysis of the face image that

represents a face as a weighted combination of a number of canonical faces

(Delac and Grgic 2004).

The identification of a person by his facial image can be done in a

number of different ways such as by capturing an image of the face in the

visible spectrum using an inexpensive camera or by using the infrared

patterns of facial heat emission. Facial recognition in visible light typically

models key features from the central portion of a facial image. Using a wide

assortment of cameras, the visible light systems extract features from the

29

captured images that do not change over time while avoiding superficial

features such as facial expressions or hair (Podio and Dunn 2002).

Some of the challenges of facial recognition in the visual spectrum

include reducing the impact of variable lighting and detecting a mask or

photograph. Some facial recognition systems may require a stationary or

posed user in order to capture the image though many systems use a real time

process to detect a person head and locate the face automatically. Major

benefits of facial recognition that it non-intrusive, hands-free and continuous

accepted by most users (Podio and Dunn 2002). The types of biometric

characteristics use in various security applications as shown in Figure 2.2.

Figure 2.2 Types of Biometric Characteristics

(a) Face, (b) Fingerprint, (c) Hand geometry, (d) Iris, (e) Retina,

(f) Signature and (g) Speaker

30

2.2.2 Fingerprints

The patterns of friction ridges and valleys on an individual fingertip

are unique to that individual (Clancy et al. 2003). For decades law

enforcement has been classifying and determining identity by matching key

points of ridge endings and bifurcations. Fingerprints are unique for each

finger of a person including twins. One of the most commercially available

biometric technologies fingerprint recognition devices for desktop and laptop

access are now widely available from many different vendors at a low cost.

With these devices users no longer need to type passwords, instead only a

touch provides instant access. Fingerprint systems can also used in

Identification Mode. Several states check fingerprints for new applicants to

social services benefits to ensure recipients do not fraudulently obtain benefits

under fake names (Cappelli et al. 2006).

Fingerprints are unique to each individual and each individual has

his own pattern in his fingerprints. This type of identification has been

successfully used by the police to capture criminals and to find missing

children. A fingerprint records the patterns found on a fingertip. There are a

variety of approaches to fingerprint verification. The traditional method,

which is used by police, matches minutiae (details of the fingerprint). Some

other approaches are pattern matching and more patterns of 3 borders. There

are some verification approaches that can detect if a live finger is presented,

but not all of these approaches can provide this type of information. If

fingerprint-scanning techniques were to be incorporated into the flight deck to

provide continuous authentication, liveness detection or testing would be a

requirement for the system (Pankanti et al. 2002).

31

2.2.3 Head Geometry

These methods of personal authentication are well established.

Hand recognition has been available for over twenty years. To achieve

personal authentication a system may measure either physical characteristics

of the fingers or the hands. These include length, width, thickness and surface

area of the hand. One interesting characteristic is that some systems require a

small biometric sample (a few bytes). Hand geometry has gained acceptance

in a range of applications. It can frequently be found in physical access

control in commercial and residential applications in time and attendance

systems and in general personal authentication applications (Podio and Dunn

2002).

Hand geometry involves analyzing and measuring the shape of the

hand. This type of biometric offers a good balance of performance

characteristics and is relatively easy to use. The ease of integration into other

systems and processes, coupled with ease of use, makes hand geometry an

obvious first step for many biometric projects. Unlike fingerprints, the human

hand isn’t unique. It is also known that one could change the geometry of

their hands by taking a hammer and smashing it. One drawback for this type

of identification is that individual hand features are not descriptive enough for

identification. Hand geometry is the granddaddy of the modern biometrics by

virtue of a 20-year history of live applications. There have been six different

hand scanning products developed over this span, including some of the most

commercially successful biometrics to date (Ip and Yin 1996). Hand

geometry biometric is by far less accurate than other biometric methods.

As an extension to hand geometry analysis, a recent creation by

Live Grip analyzes the veins, arteries and fatty tissues of the hand. Sixteen

scans are taken and a template of the individual’s hand is stored. This method

of identification could be costly in terms of storage of templates because

32

sixteen scans are taken, but at the same time, this method does analysis of

distinct characteristics of an individual that cannot be changed (that is, vein

geometry, arteries and fatty tissues of the hand). Hand geometry based

systems are used to authenticate airport employees for almost 10 years. Hand

geometry is also used to track movements of its prisoners, staff and visitors

within prisons (Huang et al. 2002).

2.2.4 Iris Recognition

This recognition method uses the iris of the eye which is the

colored area that surrounds the pupil. Iris patterns are thought unique. The iris

patterns are obtained through a video based image acquisition system. Iris

scanning devices have been used in personal authentication applications for

several years. Systems based on iris recognition have substantially decreased

in price and this trend is expected to continue. The technology works well in

both verification and identification modes (in systems performing one-to-

many searching in a database). Current systems can be used even in the

presence of eyeglasses and contact lenses. The technology is not intrusive. It

does not require physical contact with a scanner. Iris recognition has been

demonstrated to work with individuals from different national groups and

nationalities (Daugman 2004).

A retina-based biometric involves analyzing the layer of blood

vessels situated at the back of the eye. This technique uses a low- intensity

light source through an optical coupler to scan the unique patterns of the

retina. Retinal scanning can be quite accurate but does require the user to look

into a receptacle and focus on a given point (Podio and Dunn 2002). This

technique may pose a problem if the subject wears glasses or if the subject is

concerned with having close contact with the retinal reading device. It is also

unknown what types of results are presented in a situation when the user has

an eye disease such as cataracts.

33

2.2.5 Signature Verification

Biometric signature verification goes beyond visual signature

comparison in its analysis of the way a user signs his/her name. Signing

features such as speed, velocity, and pressure are as important as the finished

signature static shape. Signature verification devices are reasonably accurate

in operation and obviously lend themselves to applications where a signature

is an accepted identifier. Every person has a unique signature but that

signature is still vulnerable to duplication. If one person tries to ‘forge’ a

signature, they will study their victim’s signature and practice that style of

writing. However, since speed, velocity, and pressure play a role in signature

verification, an attacker would need to know these characteristics prior to

attempting to forge a biometric signature (Bromme 2003).

Today, computer use is expanding to every corner of the world.

Until now, the computer infrastructure was simply not ready for biometrics or

signature verification. Digital signature verification is relatively new and has

begun its history within the last 1 - 2 years. In the past, simply looking at two

or more samples of a person’s signature to see if they matched was signature

verification. By performing digital signature verification, matching is done by

comparing the movement of how one signs his/her name as mentioned above

(Liu and Silverman 2000).

Individuals are familiar with signature and voice verification

methods as a means of identification verification on a daily basis. The

accuracy of signature verification cannot be ensured. A signature may change

depending of various factors such as arthritis, temperature of the hand or

stress levels. This is the same for voice authentication because any type of

background noise or sickness (such that soar throat) may affect accuracy

(Bromme 2003). Both of these methods are widely accepted but do not

provide the type of security necessary in the flight deck of a plane. This

34

premature assumption does not state that voice and signature verification

methods cannot be used in conjunction with other methods to provide

continuous authentication in the mobile devices. Biometrics will not serve as

a replacement technology, but it will serve as an enhancement. Layered with

existing access control systems, it may provide an exceptional level of

security for both the public and private sectors (Woodward et al. 2003).

This technology uses the dynamic analysis of a signature to

authenticate a person. The technology is based on measuring speed, pressure

and angle used by the person when a signature is produced. One focus for this

technology has been electronic business applications and other applications

where signature is an accepted method of personal authentication (Podio and

Dunn 2002).

2.2.6 Speaker Recognition

Speaker recognition has a history dating back some four decades

where the outputs of several analog filters were averaged over time for

matching. Speaker recognition uses the acoustic features of speech that have

been found to differ between individuals (Chibelushi et al. 2002). These

acoustic patterns reflect both anatomy (that is, size and shape of the throat and

mouth) and learned behavioural patterns (that is, voice pitch, speaking style).

This incorporation of learned patterns into the voice templates (the latter

called ‘voiceprints’) has earned speaker recognition and its classification as a

‘Behavioural Biometric’. Speaker recognition systems employ three styles of

spoken input are: Text Dependent, Text Prompted and Text Independent.

Most speaker verification applications use Text Dependent input which

involves selection and enrollment of one or more voice passwords. Text

Prompted input is used whenever there is concern of imposters. The various

technologies used to process and store voice prints include Hidden Markov

Models (HMM), Pattern Matching Algorithms (PMA), Neural Networks

35

(NN), Matrix Representation (MR) and Decision Trees (DT). Some systems

also use ‘anti-speaker’ techniques such as cohort models and world models

(Podio and Dunn 2002).

Ambient noise levels can impede both collections of the initial and

subsequent voice samples. Performance degradation can results from changes

in behavioural attributes of the voice and from enrollment using one

telephone and verification on another telephone. Voice changes due to aging

also need to be addressed by recognition systems. Many companies market

speaker recognition engines, often as part of large voice processing, control

and switching systems. Capture of the biometric is seen as noninvasive (Delac

and Grgic 2004). The technology needs little additional hardware by using

existing microphones and voice transmission technology allowing recognition

over long distances via ordinary telephones (wire line or wireless).

2.3 GEOMETRIC BIOMETRY

In geometric feature-based systems, major face components and/or

feature points are detected in the images. The distances between feature points

and the relative sizes of the major face components are computed to form a

feature vector. The feature points can also form a geometric graph

representation of the faces. Feature based techniques are usually

computationally more expensive than template-based techniques, but are

more robust to variation in scale, size, head orientation and location of the

face in an image. The work to be described in this paper is, to some extent, a

hybrid approach. The first locate a set of feature points, and then extract a set

of Gabor wavelet coefficients at each point through image convolution.

Identifying human facial expressions has become an important field

of study in recent years because of its inherent intuitive appeal and also due to

possible applications such as human computer interaction, face image

36

compression, synthetic face animation and video facial image queries. While

approaches based on 3D deformable facial model have achieved expression

recognition rates of as high as 98% (Essa et al. 1994), they are

computationally inefficient and require considerable a priori training based on

2D information, which is often unavailable. Recognition from 2D images

remains a difficult yet important problem for areas such as image database

querying and classification. The accuracy rates achieved for 2D images are

around 90% (Bourel et al. 2002). In a recent review of expression recognition,

(Fasel and Luettin 2003) considers the problem along several dimensions:

whether features such as lips or eyebrows are first identified in the face

(Black and Yacoob 1997) or whether the image model used is 2D. Methods

proposed for expression recognition from 2D images include the Gabor-

Wavelet (Zhang and Ji 1998) or Holistic Optical flow (Yacoob and Davis

1996) approach.

Geometry facial expression recognition from image sequences

using 2D appearance-based local approach for the extraction of intransient

facial features, that is, features of eyebrows, lips or mouth, which are always

present in the image, but may be deformed (Fasel and Luettin 2003), in

contrast transient features are wrinkles or bulges that disappear at other times.

The main advantages of such an approach is low computational requirements,

ability to work with both colored and grayscale images and robustness in

handling partial occlusions (Bourel et al. 2002).

The system detects open and closed state of the mouth as well. The

algorithm presented here works on both color and grayscale image sequences.

An important aspect of this research work to be use of the color information

for robust and more accurate segmentation of lip region in case of color

images (Zhu et al. 2000). The novel lip-enhancement transform is based on

Gaussian modeling of skin and lip color. To place the work in a larger context

of face analysis and recognition, the overall task requires that the part of the

37

image involving the face be detected and segmented. We assume that a near-

frontal view of the face is available. Tests on a grayscale and two color face

image databases (Kanade et al. 2000) demonstrate a superior recognition rate

for four facial expressions (happiness, sadness, fear, anger, surprise and

disgust against neutral).

2.4 FACIAL EXPRESSION RECOGNITION MODELS

Facial expression is a visible manifestation of the affective

state, cognitive activity, intention, personality, and psychopathology of a

person (Donato et al. 1999), it plays a communicative role in

interpersonal relations. Facial expressions, and other gestures, convey

non-verbal communication cues in face-to-face interactions. These cues

may also complement speech by helping the listener to elicit the intended

meaning of spoken words. It is (Pantic and Rothkrantz 2000) reported that

facial expressions have a considerable effect on a listening interlocutor,

the facial expression of a speaker accounts for about 55 percent of the

effect, 38 percent of the latter is conveyed by voice intonation and 7 percent

by the spoken words.

As a consequence of the information that they carry, facial

expressions can play an important role wherever humans interact with

machines. Automatic recognition of facial expressions may act as a

component of natural human machine interfaces (Dam 2000) (some

variants of which are called perceptual interfaces (Pentland 2000) or

conversational interfaces (Zue and Glass 2000). Such interfaces would

enable the automated provision of services that require a good

appreciation of the emotional state of the service user, as would be the case

in transactions that involve negotiation, for example. Some robots can

also benefit from the ability to recognize expressions.

38

Automated analysis of facial expressions for behavioural

science or medicine is another possible application domain (Donato et al.

1999, Essa and Pentland 1997). From the viewpoint of automatic

recognition, a facial expression can be considered to consist of

deformations of facial components and their spatial relations, or

changes in the pigmentation of the face. Research into automatic

recognition of facial expressions addresses the problems surrounding the

representation and categorization of static or dynamic characteristics of

these deformations or face pigmentation.

2.4.1 Eigenface and Eigenvalues

Eigenfaces are a set of eigenvectors used in the computer vision

problem of human face recognition. These eigenvectors are derived from the

covariance matrix of the probability distribution of the high-dimensional

vector space of possible faces of human beings. Facial recognition was the

source of motivation behind the creation of eigenfaces (Pentland 2000). For

this use, eigenfaces have advantages over other techniques available, such as

the system’s speed and efficiency. Using eigenfaces is very fast, and able to

functionally operate on lots of faces in very little time. Unfortunately, this

type of facial recognition does have a drawback to consider: trouble

recognizing faces when they are viewed with different levels of light or

angles. For the system to work well, the faces need to be seen from a frontal

view under similar lighting. Face recognition using eigenfaces has been

shown to be quite accurate.

Facial recognition is the source of motivation behind the creation of

eigenfaces. The process of using eigenfaces is very fast and able to

functionally operate on lot of faces in very little time. Unfortunately, this type

of facial recognition does have a drawback to face recognizing when they are

39

viewed with different levels of light or angles. For the system to work well,

the faces need to be seen from a frontal view under similar lighting. Face

recognition using eigenfaces has been shown to be quite accurate. By

experimenting with the system to test it under variations of certain conditions,

the following correct recognitions were found: an average of 96% with light

variation, 85% with orientation variation and 64% with size variation (Turk

and Pentland 1991).

Face image to be seen as vectors whose components are the

brightnesses of each pixel. The dimension of this vector space is the number

of pixels. They are very useful for expressing any face image as a linear

combination of some of them. In the facial recognition branch of biometrics,

eigenfaces provide a means of applying data compression to faces for

identification purposes. In the FERET database, images may vary because of

differences in illumination, facial expression, style of glasses and even small

changes in viewpoint, none of which are relevant to the task of identifying the

image subject. The problem knows which Eigenvectors correspond to useful

information and which are simply meaningless variation (Phillips et al. 2000).

The face images of specific Eigenvectors, it is sometimes possible

to determine what features are encoded in that Eigenvector. Face images of

the Eigenvectors used in the FERET evaluation of Eigenvalue. Eigenvector

two encode lighting from right to left and Eigenvector two apparently encodes

lighting from top to bottom. Since the probe and gallery images of a single

subject may not have the same lighting, it is reasonable to assume that

removing these Eigenvectors might improve performance, by removing noise

(Phillips et al. 2000).

40

2.4.2 Fisherface Model

Edge projection analysis used for feature extraction is eyebrows

and lips. To describe a template based matching an essential starting point, is

use of contours analysis. This combines facial metrics (measuring distance

between facial features) with the eigenface approach. Another method which

is competing with the eigenface technique uses fisherfaces. This method for

facial recognition is less sensitive to variation in lighting and pose of the face

(Liu and Wechsler 2002).

Face descriptors based on Gabor filtering have been recognized as

one of the most successful representation methods, such as Elastic Graph

Matching (EGM) (Wiskott et al. 1997), Gabor Fisher Classifier (GFC) (Liu

and Wechsler 2002) and AdaBoost Gabor Fisher Classifier (AGFC) (Yang

et al. 2004). In Elastic Bunch Graph Matching (EBGM), Gabor wavelets were

firstly exploited to model faces based on the multi-resolution and multi-

orientation local features. Until now, face representation based on Gabor

features have achieved great success in face recognition area for the variety of

advantages of the Gabor filters. In (Liu and Wang 2006), proposed Gabor

Fisher Classifier method and achieved good performance. In this method,

fisher linear discriminant analysis was applied on the Gabor features extracted

from the grid vertices of the face images. However, Gabor features are too

high dimensional and need to be reduced. For instance, in GFC, the high

dimensional Gabor features were down-sampled and further reduced

dimension by using Principal Component Analysis (PCA). In, Yang et al.

(2004) proposed a feature selection method based on AdaBoost to select the

most discriminating Gabor features as a more compact face representation for

following classification, that is Fisher Discriminant Analysis (FDA) (Shin and

Chuang 2004). Nevertheless, in this method, in order to preserve enough

information for classification, the dimension of selected Gabor features is still

41

as high as several thousands. From the other hand, the maximum dimension

of FDA subspace is not larger than the number of subjects in the training set,

which are generally only several hundreds (Bolle et al. 2003).

Therefore, only hundreds of dimension can be modeled in the FDA

subspace, which means plenty of discriminative information has to be

discarded as the dimension of FDA space is far less than the number of

features used for training. The method based on multiple FDAs increase the

total dimension of FDA subspaces by grouping the AdaBoosted Gabor

features. The system of training multiple FDA classifiers comprises three

stages. Firstly, the use of AdaBoost to select thousands of most discriminating

Gabor features as in (Yang et al. 2004). Face recognition is a multi-class

problem; therefore, in order to using AdaBoost, the use of this concept to the

intra-personal and extra-personal difference (Moghaddam et al. 1998) to

convert the multi-class problem to a two-class problem. Then, divide the

training set consists of intra-personal and extra-personal differences into

several subsets by random sampling. On each subset, can be obtain a subset of

Gabor feature selected by AdaBoost. Secondly, the re-group this features to

form larger subsets. Finally, multiple classifiers are obtained by applying

FDA on each feature subset. In the testing process, a face image is classified

by each of the FDA classifier. Extensive experiments on Face Recognition

Technology (FERET) databases (Phillips et al. 2000) and Chinese Academy

of Science-Pose, Expression, Accessories and Lighting (CAS-PEAL)

databases (Cao et al. 2004) have shown that the proposed method achieves

better performance compared with using the single FDA method.

To complement eigenfaces, another approach has been developed

called eigenfeatures. This combines facial metrics (measuring distance

between facial features) with the eigenface approach. Another method, which

is competing with the eigenface technique, uses fisherfaces. This method for

42

facial recognition is less sensitive to variation in lighting and pose of the face

than the method using eigenfaces (Liu and Wechsler 2002). A more modern

alternative to eigenfaces and fisherfaces is the active appearance model,

which decouples the face’s shape from its texture: it does an eigenface

decomposition of the face after warping it to mean shape. This allows it to

perform better on different projections of the face and when the face is tilted.

A number of fields of psychology and biometric security have been interested

in facial expressions of emotion. Social psychologists studying person

perception have often focused on the face (Cappeli et al. 2006). The

examining relative weight given to the face as compared to other sources of

information, the relationship between encoding and decoding and individual

differences.

2.4.3 Three Dimension (3D) Face Model

Among the many biometric identification modalities proposed for

verification and identification purposes, face recognition is high in the list of

subject preference, mainly because of its non-intrusive nature. However, from

the operator’s point of view, face recognition faces some significant

challenges that hamper its widespread adoption (Ip and Yin 1996). Accuracy

is the most important of these challenges. Current 2D face recognition

systems can be confounded by differences in pose, lighting, expressions and

other characteristics that can vary between captures of a human face. This

issue becomes more significant when the subject has incentives not to be

recognized (that is, non-cooperative subjects). It is now widely accepted that

in order to address the challenge of accuracy, different capture modalities

(such as 3D or infrared) and/or multiple instances of subjects (in the form of

multiple still captures or video) must be employed (Bowyer et al. 2006).

However, the introduction of new capture modalities brings new challenges

for a field-deployable system. The challenges of 3D face recognition are:

43

1. Accuracy Gain: A significant accuracy gain compared to 2D

face recognition systems must result to justify the introduction

of a 3D system, either for sole use or in fusion with other

modalities.

2. Efficiency: 3D capture creates larger data files per subject

which implies significant storage requirements and slower

processing. The conversion of raw 3D data to efficient meta-

data must thus be addressed.

3. Automation: A field-deployable system must be able to

function fully automatically. It is therefore not acceptable to

assume user intervention for locating key landmarks in a 3D

facial scan.

4. Capture Devices: 3D capture devices were primarily

developed for medical and other low-volume applications and

suffer from a number of drawbacks when applied to face

recognition. These include artifacts, small depth of field, long

acquisition time, multiple types of output, and high price.

5. Testing Databases: There is a lack of large and widely

accepted databases for objectively testing the performance of

3D face recognition systems.

The major challenges of a 3D field-deployable face recognition

system leads to the development of fully automatic system which uses a

composite alignment algorithm to register 3D facial scans with a 3D facial

model, thus achieving complete pose-invariance. In this system employs a

deformable model framework to fit the 3D facial model to the aligned 3D

facial scans, and in so doing measures the difference between the facial scan

and the model in a way that achieves a high degree of expression invariance

and thus high accuracy. The 3D differences (the deformed facial model) are

44

converted to a 2D geometry image and then transformed to the wavelet

domain; it has been observed that a small portion of the wavelet data is

sufficient to accurately describe a 3D facial scan, thus achieving the

efficiency goal. Some of the issues of 3D capture devices were addressed;

specifically, artifacts were handled by median cut and smoothing filters

(Papatheodorou and Rueckert 2007).

To develop a multistage hybrid mesh alignment algorithm to

initialize the model fitting process, develop a parameterized, annotated,

anthropometrically-based, subdivision deformable face model which is used

for identifying the facial features from the raw 3D data and finally introduce a

novel distance metric based on the wavelet representation of the geometry

image and normal map corresponding to the subdivided and deformed face

model. The result of these contributions is a system for 3D face recognition

that achieves the highest accuracy on the Face Recognition Grand Challenge

(FRGC) database (Phillips et al. 2005).

2.5 CONTRIBUTION OF THE THESIS

Developmental psychologists are examining the age at which

infants first show what can be considered as an emotion, whether this age

precedes or follows an infant’s ability to recognize emotions, and the

sequencing of expressions between caregiver and infant. Physiological

psychologists have been concerned with the role of the right hemisphere in

the recognition and more recently, in the production of facial expression, and

in the relationship between facial and autonomic measures of arousal (Bourel

et al. 2002). Many divergent questions that involve consideration of facial

expression were subject to considerable research a few decades ago, although

sometimes the questions were phrased differently. Unfortunately, little

progress was made. The most basic questions were not answered, and

methods for measuring facial expression were not well developed. In the last

45

decade, progress has been made both on methods and on a set of fundamental

(Bourel et al. 2002).

Some theorists have focused on the power of emotional expression

to convey messages about the expresser as the center of their theories about

emotion. Emotional expression and especially facial expressions, into a

modern scientific treatment in the mid-nineteenth century and provided a

basis for considering facial expressions as behaviours that evolved as a

mechanism of communication. Although emphasis on the communicative

potential of facial expression of emotion as an object of adaptive selection,

the thrust of his general work suggests this connection and encouraged later

scientists to elaborate upon this mechanism (Krinidis et al. 2003).

In the literature review of geometric feature based neural network

approach (Tian et al. 2001) requires action unit combinations for its

recognition rate without any expressional value linked to it. However in this

research genetic algorithm based facial recognition, expressional parameters

are associated to geometric features to have better recognition rate. In addition

the (Tian et al. 2001) focus on single action unit and combinations action

units without any specific variations in the extracted feature, whereas this

research work specifically mentioned the expressional elements in terms of its

geometric facial component variations by its gene mapping value sets (width,

length and height of lip, nose, eye). With this invariant feature extraction

provides a better recognition rate in research model of genetic algorithm

influenced by gene property.

The proposed method in this research work, deals with two types of

features extraction that is, geometric features and behavioural features.

Geometric features present the shape and locations of facial components

(including mouth, eyes, brows and nose). The facial components or facial

feature points are extracted to form a feature vector that represents the face

46

geometry. Experimental proceeding shows that using hybrid features can

achieve better results for some expressions. To remove the effects of variation

in face scale, motion, lighting and other factors, one can first align and

normalize the face to a standard face (2D or 3D) manually or automatically

and then obtain normalized feature measurements by using a reference image

(neutral face).

The majority of the work in the area of expressional behavioural

recognition focuses on recognizing a set of basic emotions by observing

patterns of physiological activity. The works presented both color and

grayscale image sequences. Tests on a grayscale and two color face image

databases demonstrate a better recognition rate for six facial expressions such

as happiness, sadness, fear, anger, surprise and disgust. Experimental

evaluation using hybrid features achieve better results in facial expressions.

To remove the effects of variation in face scale, motion, lighting and other

factors, the face is first aligned and normalized to a standard face manually or

automatically and then the normalized feature measurements are obtained by

using a reference image that is neutral face. The face expressions recognition

is based on geometric features and template matching. The experimental

results demonstrate the geometric features based on approach that can be

effectively explored for a coarse preliminary face recognition stage. Then the

final recognition, more precise technique of behavioural parametric of

expression matching is applied as a new low level approach to gray scale

image similarity evaluation (Kwon and Moon 2008).

The contributions of this research work summarized are initially

facial acquisition is carried in its normal strides with the effective extraction

of facial geometric features by eliminating noises and smoothening and

sharpening the face component edges (eyes, nose and lips). The facial

expressions are measured through facial action units by interpreting its

47

behavioural aspect behind the basic six expressions of human being. The

facial expressions due to behavioural patterns expressions are associated to its

respective physiological geometric property changes in the facial parts. With

growing variations of physiological properties to human expressions for

different human, genes are generated to store the mapping characteristics for

all the basic expressions of each individual to be under testing of facial

expressional recognition. Effective recognition is done through the

application of genetic algorithm on expressional gene operation carried out in

individual human face subjected to recognition.