DEVELOPMENT OF EFFICIENT BIOMETRIC RECOGNITION ALGORITHMS BASED ON FINGERPRINT...

DEVELOPMENT OF EFFICIENT BIOMETRIC

RECOGNITION ALGORITHMS BASED ON

FINGERPRINT AND FACE

A thesis submitted to the Christ University for the Degree of

DOCTOR OF PHILOSOPHY

IN

COMPUTER SCIENCE

BY

JOSSY P. GEORGE

UNDER THE GUIDANCE OF

Dr. K. B. RAJA

Centre for Research

Christ University, Bangalore - 560029

MARCH – 2012

I

CERTIFICATE

This is to certify that the thesis entitled DEVELOPMENT OF

EFFICIENT BIOMETRIC RECOGNITION ALGORITHMS

BASED ON FINGERPRINT AND FACE submitted by Jossy P.

George to Christ University, Bangalore for the award of the degree of

Doctor of Philosophy is a bonafide record of research work carried out by

Jossy P. George under my supervision. The contents of this thesis, in

full or its parts, have not been submitted to any other University for the

award of any degree or diploma.

Place: Bangalore Dr. K. B. Raja

Date: 23 March, 2012 Associate Professor

Department of Electronics and

Communication Engineering

University Visvesvaraya College of

Engineering, Bangalore

II

DECLARATION

I hereby declare that Ph. D thesis titled DEVELOPMENT OF

EFFICIENT BIOMETRIC RECOGNITION ALGORITHMS

BASED ON FINGERPRINT AND FACE is an original research work

done by me under the guidance and supervision of Dr. K. B. Raja,

Associate Professor, Department of Electronics and Communication,

University Visvesvaraya College of Engineering. This thesis is submitted

to Christ University, Bangalore, for the award of the degree of DOCTOR

OF PHILOSOPHY IN COMPUTER SCIENCE.

I also declare that this thesis or any part of it has not been submitted to

any other university for the award of any degree.

Place : Bangalore Jossy P. George

Date : 23 March, 2012

III

ACKNOWLEDGEMENTS

First and foremost I raise my heart in gratitude to God my Father for

His inspiration and unending love.

This research is incomplete if I do not place on record my deepest

sense of gratitude to all those who inspired me, guided and assisted me to

take up and conduct this research.

I wish to express my heart felt and deep debt of gratitude to my

research supervisor and guide Dr. K. B. Raja, Associate Professor,

Department of Electronics and Communication, University Visvesvaraya

College of Engineering for his invaluable guidance and direction

throughout my research. He has been a constant source of inspiration and

it would not have been possible but for his entirely effort in guiding me to

complete this thesis. His patience to go through my work, gentle

encouragement and his continuous guidance has inspired me a lot. His

clarity of thought, incisive analysis and taste for perfection are qualities

that are worth being emulated.

I accord my sincere thanks to Dr. Fr. Thomas C. Mathew, Vice

Chancellor, Christ University, Dr. Fr. Abraham V. M., Pro Vice

Chancellor, Christ University, Prof. J. Subramanian, Registrar, Christ

University, Dr. Fr. Varghese K. J, Chief Finance Officer and all other

CMI fathers of Christ University for their constant support and

encouragement.

I owe my heartfelt and special gratitude to Dr. Srikanta Swamy,

Additional Director, Centre for Research, Christ University, for his deep

concern and timely directions throughout my research work.

IV

I thank my beloved father Late Mr. Paul George and my mother Mrs.

Mary George, who moulded me in my academic pursuit and character

formation. I express my sincere gratitude to my brothers and sisters for

their encouragement. I would like to thank the Librarian, Christ

University, all the faculty members of my department, my colleagues and

friends in Christ University for their whole hearted support.

Finally, my sincere acknowledgements to all who directly or

indirectly helped me in completion of this work.

Jossy P. George

V

PREFACE

The reliable verification systems are required to verify and confirm

the identity of an individual requesting their service. Secure access to the

buildings, laptops, cellular phones, ATM etc. is an example of such

applications. In the absence of robust verification systems, these systems

are vulnerable to the wiles of an impostor. The traditional ways of

authentications are passwords (knowledge – based security) and the ID

Cards (token – based security). These methods can be easily breached due

to the chance of stolen, lost or forget. The development and progress of

biometrics technology, the fear of stolen, lost or forget can be eliminated.

Biometrics refers to the automatic identification (or verification) of an

individual (or a claimed identity) by using certain physiological or

behavioral traits associated with the person.

The biometrics identifies the person based on features vector derived

from physiological or behavioural characteristics such as uniqueness,

permanence, accessibility, collectability with minimum cost. The

physiological biometrics are Fingerprint, Hand Scan, Iris Scan, Facial

Scan and Retina Scan etc., and behavioural biometric are Voice,

Keystroke, Gait, Signature etc., The physiological biometrics measures

the specific part of the structure or shape of a portion of a subject’s body.

VI

But the behavioural biometric are more concerned with mood and

environment.

Chapter one presents the introduction to biometrics and its various

traits. Further description like structure of the biometric system, different

approaches are discussed. Also the design issues in biometric system such

as universality, collectability, distinctiveness, permanence, acceptability,

uniqueness, performance, circumvention etc., are discussed.

Chapter two gives a detailed survey of biometric techniques. It

includes the literature survey of fingerprint and face biometric traits and

various approaches.

In Chapter three, the algorithm of Fingerprint Verification based on

Dual Tree Complex Wavelet Transformation (DTCWT) is proposed. The

original fingerprint is cropped and resized to apply the DTCWT. The

features of Fingerprint are obtained by applying different levels of

DTCWT. Performance analysis is discussed with the FRR, FAR and TSR.

Chapter four discusses another highly recommended source of

authentication such as face recognition. In this chapter, the algorithm of

Performance Comparison of Face Recognition using Transform Domain

Techniques (PCFTD) is proposed. The face databases L – Spacek, JAFFE

and NIR are considered. The features of face are generated using wavelet

families such as Haar, Symelt and DB1 by considering approximation

band only. The face features are also generated using magnitudes of

VII

FFTs. The test image features are compared with database features using

Euclidian Distance (ED). The performance parameters such as FAR,

FRR, TSR and EER computed using wavelet families and FFT. The

methodology described in this paper is accurate, simple, fast and better

than the existing algorithms. Chapter five presents conclusion and future

work.

VIII

LIST OF TABLES

Sl. Table Title Page

No No No

01 3.1 Scanners / Technologies used for FVC 2004 63

02 3.2 Proposed TDFID Algorithm 70

03 3.3 EER and TSR for Different Levels of DTCWT 71

04 3.4 Values of FRR, FAR and TSR for Thresholds 73

05 4.1 Algorithm of PCFTD 89

06 4.2 Performance on Different Face Databases with FFT 91

07 4.3 Performance Parameters of L- Spacek Databases 95

08 4.4 Performance Parameters of JAFFE Databases 96

09 4.5 Performance Parameters of NIR Databases 97

10 4.6 EER Values for Different Transforms 103

IX

LIST OF FIGURES

Sl Figure Title Page

No No No

01 1.1 Classification of Biometrics 03

02 1.2 Fingerprint Ridge Patterns 05

03 1.3 Fingerprint Characteristics 05

04 1.4 A Face Scan 06

05 1.5 Ear Structure 06

06 1.6 DNA Structure 08

07 1.7 Structure of Palm Image 08

08 1.8 How Iris Scanners Record Identities 10

09 1.9 Front View of Iris 10

10 1.10 Retina 11

11 1.11 Gait Cycle 12

12 1.12 Signature 12

13 1.13 Keystroke Dynamics 13

14 1.14 Voice File 14

15 1.15 General Biometrics System 14

16 1.16 The Original Histogram of a Fingerprint Image 18

17 1.17 Histogram after the Histogram Equalization 18

18 1.18 The Fingerprint Image after Adaptive Binarization 19

19 3.1 TDFID Model 61

20 3.2 One Fingerprint Image from Each Database 64

21 3.3 A Sample of Finger Print of DB3_A 64

22 3.4 Real and Imaginary Parts - Complex Coefficients 66

23 3.5 DTCWT Images at Different Levels. 66

24 3.6 Variations of FRR, FAR and TSR with Threshold 72

X

25 4.1 The Block Diagram of PCFTD Model 76

26 4.2 Samples of NIR Fface Images of a Person 78

27 4.3 Samples L- Spacek Face Images of a Person 79

28 4.4 Samples JAFFE Face Images of a Person 80

29 4.5 Wavelet Families 82

30 4.6 Wavelet Decomposition 86

31 4.7 Block Diagram of 2 DWT Decomposition Process 87

32 4.8 FAR and FRR with Threshold (L-Spacek with FFT) 92

33 4.9 FAR and FRR with Threshold (JAFFE with FFT) 93

34 4.10 FAR and FRR with Threshold (NIR with FFT) 93

35 4.11 FAR and FRR with Threshold for L–Spacek

Databases with DWT (Haar) 98

36 4. 12 FAR and FRR with Threshold for L–Spacek

Databases with DWT (Symlet) 98

37 4.13 FAR and FRR with Threshold for L–Spacek

Databases with DWT (DB1) 99

38 4.14 FAR and FRR with Threshold for JAFFE






41 4.17 FAR and FRR with Threshold for NIR






TABLE OF CONTENTS

Certificate I

Declaration II

Acknowledgement III

Preface V

List of Tables VIII

List of Figures IX

CHAPTER 1

INTRODUCTION

1.1 Introduction 01

1.2 Types of Biometrics 02

1.3 The Biometric System 14

1.4 Design Issues in Biometric System 25

1.5 Applications of Biometrics System 26

1.6 Definitions 28

1.7 Motivation 30

1.8 Organization of the Thesis 30

CHAPTER 2

LITERATURE SURVEY

2.1 Introduction 31

2.2 Review of Fingerprint Biometric Trait 31

2.3 Review of Face Biometric Recognition 49

2.4 Summary 59

V

CHAPTER 3

TRANSFORM DOMAIN FINGERPRINT

IDENTIFICATION BASED ON DTCWT

3.1 Introduction 60

3.2 Proposed Model 60

3.3 Algorithm 69

3.4 Performance Analysis 70

3.5 Summary 74

CHAPTER 4

PERFORMANCE COMPARISON OF FACE

RECOGNITION USING TRANSFORMS DOMAIN

TECHNIQUES (PCFTD)

4.1 Introduction 75

4.2 Proposed PCFTD Model 76

4.3 Algorithm 89

4.4 Performance Analysis 90

4.5 Summary 104

CHAPTER 5

CONCLUSIONS

5.1 Introduction 105

5.2 Contribution of this Work 106

5.3 Future Work 107

Bibliography 108

List of Publications 129

Appendix A 130

Appendix B 145

VI

CHAPTER 1

INTRODUCTION

1

CHAPTER 1

INTRODUCTION

1.1 INTRODUCTION

The Biometric algorithms are used for analysing human body parts to

authenticate a person for multiple applications. The word Biometric is

derived from the Greek word [1] bios (life) and metrikos (measure). The

biometric system captures and store certain human body parts ie.,

biometric information onto the database and compares the test biometric

information with the database biometric information. The biometric

system has received considerable attention and has been successfully used

in many applications. The earlier biometric systems used for

authentication are unimodel that depends only on single source of

biometrics information whereas now a days multimodel biometrics are

used depends on multiple biometrics. Biometrics are becoming more

popular now a day’s, due to the security requirements in the field of

information, business, military, e-commerce, internet and electronic

transfers. In the mid nineteenth century the police criminal identification

division in Paris [2] have developed and practiced the idea of using many

features of human body parts and behavioral characteristics to identify

criminals. Since then biometric recognition technology emerged rapidly

in law enforcement to identify criminals. The personal identification

2

based on biometrics is essential to create Unique Identification (UID)

card, which can be used for voting in electoral system, accessing secured

areas, identification to avail government and nongovernment facilities.

The traditional identification systems such as passwords, employee

key, cards, Personal Identification Number (PIN) etc., can be easily

stolen, forgotten and lost, whereas biometrics cannot be stolen, forgotten

and lost, hence biometrics is more secure compared to traditional

methods. The better biometric system should satisfy the parameters of

high recognition rate, accuracy, speed, False Acceptance Rate (FAR),

False Rejection Rate (FRR), robust to fraudulent techniques and attacks,

harmless to the users and accepted by an intended users.

1.2 TYPES OF BIOMETRICS

The biometrics are broadly classified into physiological and

behavioral characteristics [3] used for automated recognition that are

based on features of fixed human body parts and behavior of a person

respectively. Classification of Biometrics is shown in the Figure 1.1. The

choice of biometrics depends on user acceptance, level of security

required, accuracy, and implementation cost and time. Among these

biometrics traits, some are required the user’s cooperation to acquire the

images for the process.

3

Fig.1.1: Classification of Biometrics

1.2.1 Physiological Parameters

Physiological characteristics are related to parts of the body such as

Fingerprint, Face, Ear, Facial Thermogram, Deoxyribo Nucleic Acid

(DNA), Hand and Palm print, Iris and Retinal blood vessel patterns.

Biometrics

Physiological Behavioral

Fingerprint Signature

Iris

Face

Hand Geometry

Keystroke

Voice

DNA

Gait

4

(i) Fingerprint

The biometric fingerprint of each person is unique and has minutia,

pores, pattern of valleys and ridges. The fingerprints have been used for

identification in forensic investigations and sciences, criminal

investigations, civil and commercial identifications since nineteenth

century. Fingerprint verification is simple, faster, cheap and reliable to

identify the individuals compared to iris, voice, retina, face and other

recognition systems. The disadvantages of fingerprint recognition are

fingerprint features may not be unique sometimes, since the injury to

finger, fingerprint of the people working in chemical industry may

change, database maintenance and protection from fraudulent user and

finally vulnerable. The fingerprint has Plain Arch, Tented Arch, Radial

Arch, Ulnar Arch, Plain Whorl, Central Pocket Whorl, Double Loop and

Accidental ridge patterns as shown in the Figure 1.2. The ridge pattern are

used to differentiate two images of persons. The fingerprint

characteristics such as Core, Ending Ridge, Short Ridge, Fork or

Bifurcation, Delta, Hook, Eye, Dot or Island, Crossover, Bridge,

Enclosures and Speciality are shown in the Figure 1.3 to compute the

features of fingerprint image.

5

Fig.1.2: Fingerprint Ridge Patterns.

Fig.1.3: Fingerprint Characteristics

(ii) Face

The oldest method of person identification mechanisms are based on

their facial features. The Human Visual System (HVS) provides an

effective way of recognizing other people’s expressions and facial

features. Normally HVS identify people by their faces and sometimes

6

much better than computer based recognition systems. The face

recognition system shown in Figure 1.4 is nonintrusive and the most

common biometric system used now a days for personal identification.

Fig.1.4: A Face Scan

(iii) Ear

The ear shape and the structure are distinct. The features of ear size

and the pattern of ear structure is shown in Figure 1.5. The recognition is

based on matching between the landmark locations on the ear and salient

points on the pinna. The disadvantage is features of an ear are not unique.

Fig. 1.5: Ear Structure

7

(iv) Facial Thermogram

The property of human body heat radiation is considered as feature

for recognition and the heat radiation is captured by infrared camera. The

application of facial thermogram is limited to covert recognition.

(v) DNA

The biometric based on DNA sampling is a form of blood sample and

tissue and it is considered as unique feature for recognition. DNA is

probably the most reliable biometrics. It is in fact a one-dimensional code

unique for each person. The disadvantage is the patterns of DNA shown

in Figure 1. 6 may not be unique for twins and the issues such as

contamination, sensitivity, privacy and automatic real-time recognition

are not clear. DNA sampling is rather intrusive at present and requires a

form of tissue, blood or other bodily sample. The DNA analysis has not

been sufficiently automatic to rank the DNA analysis as a biometric

technology. This method, however, has some drawbacks: (i)

contamination and sensitivity, since it is easy to steal a piece of DNA

from an individual and use it for an ulterior purpose, (ii) no real-time

application is possible because DNA matching requires complex chemical

methods involving expert's skills, (iii) privacy issues since DNA sample

taken from an individual is likely to show susceptibility of a person to

some diseases.

8

Fig. 1. 6: DNA Structure

(vi) Hand and Palmprint

The structure of person's hand and palm shown in Figure 1. 7 are

unique but not as unique as iris and fingerprints. Hand scanner and finger

reader recognition system measure and analyze the pattern in the palm

and hand such as ridge length, orientation of ridges and valleys. The

technique is relatively easy, simple and inexpensive. The disadvantage is

hand and palm geometry is not unique and distinctive over the years.

Fig. 1. 7: Structure of Palm Image

9

(vii) Iris

The human iris is unique compared to other biometrics and iris image

can be captured using camera. The iris pattern contains large amount of

arbitrariness, becomes unique features and is normally formed between

the third and eighth month of fetus growth. The iris pattern remains same

throughout the life of an individual. The pattern of an iris contain many

distinctive features such as arching ligaments, furrows, ridges, crypts,

rings, corona, freckles and a zigzag collarette. Iris scanning is more

comfortable than retinal because the iris is visible from several meters

away, so very easily we can collect the image. The work on iris pattern

recognition [4] is gaining importance and it is widely used in medical

field for classification and localization of diabetic related eye diseases,

fatigue testing, security monitoring, automated access control, specific

users man- machine interface etc. Iris recognition system basically has

three stages viz., preprocessing, feature extraction and matching. In

preprocessing there are three steps – iris localization, iris normalization

and image enhancement. The localization is the process in which the

inner and outer boundaries of the iris are detected. During this process the

eyelid and eyelashes which cover the portion of the iris are removed.

Normalization is the technique in which the Cartesian coordinates are

converted to polar coordinates called as Daugman‘s rubber sheet model

[5]. But low contrast and non-uniform illumination makes the feature

10

extraction difficult. Thus the image enhancement technique provides the

uniform distribution of illumination. The two important iris recognition

methods are Daugman‘s method and Wildes [6] method. The Figure 1.8

explains how Iris scanners record identities.

Fig. 1.8: How Iris Scanners Record Identities

A front view of the iris is shown in Figure 1.9. The iris is perforated

close to its centre by a circular aperture known as the pupil. The function

of the iris is to control the amount of light entering through the pupil and

this is done by the sphincter and the dilator muscles, which adjust the

size of the pupil.

Fig. 1.9: Front View of Iris

crypts

Radial furrows

Pigment frill

Pupilary area

Ciliary area

collarette

11

(viii) Retinal blood vessel patterns

The retinal vasculature is characteristic of each individual eye and not

easy to change or replicate is as shown in Figure 1.10. The biometric

sample acquisition is difficult and usage is limited compared to other

biometrics.

Fig. 1.10: Retina

1.2.2 Behavioral Parameters

The behavioral biometrics is connected to the human behavior of a

person and investigates gesture of an individual’s such as Gait, Signature,

Keystroke, Voice etc.

(i) Gait

The way person walks and a combined spatial temporal biometric as

in Figure 1.11, and not very distinct, but satisfactorily distinguish person

in low-security application. The biometric may differ over a long period

of time due to fluctuations in body weight and major injuries connecting

joints and brain.

12

Fig. 1.11: Gait Cycle

(ii) Signature

The way person symbols his/her name is acknowledged to be a

feature of that person. The signatures have been acknowledged in lawful,

business communication and in government as a technique of verification

of person. The signature as shown in the Figure 1.12 is a behavioral

biometric usually changes over a period of time and is inclined by

physical and emotional conditions of the person. The automatic signature

confirmation system has several applications such as symbol of approval,

predominantly in credit cards validation, bank cheques, land purchases,

legal documents and security systems. The disadvantages with this system

are signature forgeries such as arbitrary, casual and skilled.

Fig. 1.12: Signature

13

(iii) Keystroke

The pressure applied by a person while typing on a keyboard, in a

unique way is referred to as keystroke given in Figure 1.13. The

keystroke is not always unique to each person but provides sufficient

discriminatory information to recognize an individual. It is observed that

there exists a deviation in typical typing patterns for some persons. The

weakness with the keystrokes of a person using a system could be

monitor and imitate.

Fig. 1.13: Keystroke dynamics

(iv) Voice

The voice biometric is a combination of both behavioral and

physiological characteristics. The individual’s dot wave files of voice are

shown in Figure 1.14. The features are based on vocal tract, mouth, nasal

cavity and lips. Thus, shape and size of the appendage are used in sound

synthesis. The physiological uniqueness of human verbal communication

are invariant for a person, but the behavioral element of the

communication of a person changes over time due to age, medical

conditions and emotional state. The voice is uniquely different including

14

twins and cannot be exactly duplicated. Voice is also not appropriate for

exact authentication of an individual because it is not very distinctive.

The disadvantage with the voice biometric system is the features of

speech may vary due to noise introduce into the system.

Fig. 1.14: Voice File

1.3 THE BIOMETRIC SYSTEM

The biometric system normally operates in two modes such as

verification mode and identification mode depends on the application.

Fig. 1.15: General Biometrics System

Accept/Reject

Classification

Section

Test Section

Feature Extraction

Preprocessing

Test Biometric

Enrollment Section

Feature Extraction

Preprocessing

Biometric Database

Match

15

Verification- The process of one-to-one comparison to claim an

identity of a person. The test image is compared with an image in the

stored database.

Identification- The process of one-to-many comparison of the test

biometric with biometric database to identify an unknown person. The

comparison of the test biometric sample with a template in the database

falls within a predefined threshold then it is succeeded in identifying an

individual. The general biometrics system is divided into three sections

viz., enrolment section, test section and classification section is shown in

Figure 1.15. The biometric database is created and preprocessed on each

biometric data to device good quality and appropriate image for

processing. The feature vectors are extracted from each biometric data

and are developed on whole biometric database in the enrollment section.

1.3.1 Enrolment Section

The biometric database is enrolled and features are extracted in this

section. The enrollment stage performs operations such as preprocessing

and feature extraction on database biometrics. Since the fingerprint

images acquired from sensors or other medias, are not assured with

perfect quality, preprocessing methods helps for increasing the contrast

between ridges and furrows and keep a higher accuracy to fingerprint

recognition.

16

1.3.1.1 Biometric Database

The significant section of any biometric system for person

identification is the data collection and creation of database. Each

component of the database is created by biometrics collected with

different image sensors such as mobile cameras, digital cameras, iPods

and web cameras at different sessions with at least two weeks’ time

separating each session and at different intensity, angle, expressions and

with or without accessories. The database is created with different images

on same person and some of the databases are readily available in the

internet.

The popular face biometric databases [7] available for research work

are Libor Spacek, AT & T (formerly Olivetti Research Laboratory), Oulu

Physics, XM2VTS, Yale, Yale-B, MIT, CMU-PIE (Pose, Illumination

and Expression), UMIST, Bern University face database, Purdue AR,

University of Sterling online database, FERET, Kuwait University face

database and AR. The fingerprint biometric databases such as Chinese

Academy of Science Institute of Automation (CASIA) developed by

Chinese Academy Institute, SFinGe, FVC2006, FVC2004, FVC2002,

FVC2000 etc. The iris biometric databases such as CASIA V2 and

UBIRIS, the palm print biometric database such as CASIA palm print

image, Hong Kong Polytechnic University 3D palm print database etc.

The signature databases are GPDS300 and SVC 2004.

17

1.3.1.2 Preprocessing

(i) Re-sizing the image

The size of an image is resized to required size or a Region Of

Interest (ROI) of an image.

(ii) Marphological Operation

Morphology is the study of the shape and form of objects.

Morphological image [ 8 ] analysis can be used to perform object

extraction, image filtering operations, such as removal of small objects or

noise from an image, image segmentation operations, such as separating

connected objects and measurement operations, such as texture analysis

and shape description.

(iii) Histogram Equalizer

The object is to enhance contrast of images using histogram

equalization. To expand the pixel value distribution of an image so as to

increase the perception information, histogram equalization is used [9, 10,

11]. The original histogram of a fingerprint image is shown in Figure

1.16. The histogram after the histogram equalization occupies all the

range from 0 to 255 and the visualization effect is enhanced as shown in

Figure 1.17.

18

Input Intensity Value Input Intensity Value

Outp

ut

Inte

nsi

ty V

alue

Ou

tpu

t I

nte

nsi

ty V

alu

e

Fig. 1.16: The Original Histogram

of a Fingerprint Image

Fig. 1.17: Histogram after the

Histogram Equalization

(iv) Binarization

It is a process in which each pixel in a given image is converted into

one bit and can assign the values '1' or '0' depending upon the mean value

of all the pixel. If greater than mean value then its '1' otherwise its '0'.

Image binarization changes an image of up to 256 gray levels to a black

and white image. The simplest and easiest way to use image binarization

is to choose a threshold value, and classify all pixels with values above

this threshold as white, and all other pixels as black. Fingerprint Image

Binarization is to convert the 8-bit gray fingerprint image to a 1-bit image

with 0-value for ridges and 1-value for furrows. After the operation,

ridges in the fingerprint are highlighted with black color while furrows

with white are shown in Figure 1.18.

19

Fig. 1.18: The Fingerprint Image after Aadaptive Binarization

(a) Binarized Image (left), (b) Enhanced Gray Image (right)

(v) Fingerprint Image Enhancement

To make the image clearer for the further processing, the fingerprint

image quality is enhanced. This happens due to the sensors or other

Medias which are not assured with perfect quality, those enhancement

methods, to improve the contrast between ridges and furrows, are very

useful to keep a higher accuracy to fingerprint recognition. Through the

fingerprint enhancement, the image is more clear for the further process

and help to draw the accurate results.

1.3.1.3 Feature Extraction

Feature extraction is the process by which the key features of the

samples are selected. The process of feature extraction is depending on

the set of algorithms; the methods choosing will be based on the type of

20

biometric identification used [ 12 ]. A fingerprint feature extraction

program is to locate, measure and encode ridge endings and bifurcations

in the fingerprint.

Some of the examples for the feature extraction are;

In a voice recording, filtering out some frequencies and patterns.

From a digital picture, it will extract particular measurements of

image patterns.

Iris prints will encode the mapping of furrows and striations in the

iris [13].

The features of each biometric are computed to identify a person and are

based on spatial domain, transform domain and fusion techniques.

(i) Spatial Domain Techniques

The biometric features are directly extracted from spatial domain

itself by applying spatial domain techniques on image pixels without

converting into frequency domain.

The Edge detection techniques: Color image is converted into gray

scale image for easy processing and then into binary image for edge

detection. The edge detection operators such as Canny, Sobel, Prewitt,

Robert etc., are used to obtain edges of an image. The features are

extracted by measuring different lengths of edges in an image [14].

21

Principal Component Analysis (PCA): The best eigenvectors of the

covariance matrix in a larger dimension data refers to the best low

dimensional space and are called as principal components of covariance

matrix [15].

Independent Component Analysis (ICA): A statistical latent variable

model called ICA [16] for computing independent components.

Line Edge Map [LEM]: LEM [17] extracts lines from an image edge

map and geometrical feature matching. Edge information is a useful

object representation feature i.e., invariance to illumination changes, low

memory requirement and high recognition performance of template

matching. LEM integrate the structural information with spatial

information of an image by grouping pixels of an image edges in to line

segments.

3D Morphable Model: The vector representation of images that is

being constructed such that any convex combination of shape and texture

vectors of a set of examples describes a realistic image is a morphable

model [18].

Hidden Morkov Model (HMM): A discrete HMM [19] models is

viewed as a probabilistic model whose states are not explicitly observed.

In HMM each state is based on the decision of probability distribution

function and symbols are emitted based on the probability of occurrence

of that symbol and depends on the previous states.

22

(ii) Transform Domain Techniques:

The biometric traits are converted from spatial domain into transform

domain i.e., frequency domain.

Fast Fourier Transform (FFT): The FFT is applied on spatial domain

image to obtain FFT coefficients. The features are extracted from FFT

[20] coefficients are real part, imaginary part, magnitude value and phase

angle. The FFT computation is fast compared to Discrete Fourier

Transform (DFT), since the number of multiplications required to

compute N-point DFT are less i.e., only N2log2

N in FFT as against N

2 in

DFT.

Discrete Cosine Transform (DCT): The technique is used for image

compression. The DCT technique [21 , 22 ] is a linear and invertible

frequency domain transform to express pixel intensity values of an image

in terms of sum of cosine functions oscillating at different frequencies.

The original spatial domain image is converted in to the frequency

domain using the DCT technique and original image is reconstructed

from DCT coefficients by applying inverse DCT technique. The

transform domain image represents original image DCT coefficients and

reflects in terms of frequencies present in it. The first DCT coefficient has

lowest frequency and forms the DC-coefficient and normally carries the

significant information of original image. The last DCT coefficient has

23

higher frequencies consists of detailed information of signal and usually

generated by noise. The rest of the coefficients carry different frequency

components of the original signal varies between very low and very high

frequency.

Discrete Wavelet Transform (DWT): The wavelet transform

represents a signal in terms of mother wavelets using dilation and

translation [ 23 ]. The wavelets are oscillatory functions having finite

duration both in time and in frequency, hence represents in both spatial

and frequency domains. The features extracted by wavelet transform

gives better results in recognition as well as in bifurcating low frequency

and high frequency components as approximation band and detailed

bands respectively.

Complex Wavelet Transform (CWT): The CWT [ 24 ] is two

dimensional wavelet to provide multi resolution and improved

transformation of DWT. CWT provides high degree of shift invariance

and has more redundancy.

Dual Tree Complex Wavelet Transform (DT-CWT): The

decomposition technique DT-CWT eliminates disadvantages of DWT,

DCT and Gabor Wavelet Transform, and gives better results in feature

extraction [25], [26]. Two wavelet trees are created in parallel forming

Hilbert pairs. The two trees of the DT-CWT are the real and imaginary

24

parts of complex wavelet. The Hilbert transform pairs in DT-CWT are

called half sample delay condition.

(iii) Fusion Techniques:

The spatial domain features extracted by different techniques are

fused to obtain final spatial domain feature vector.

The transform domain features are extracted using various

transformation techniques. The different features are combined to

generate final feature vector.

The spatial and transform domain features are combined to obtain

hybrid features with different techniques.

The different biometric traits such as speech, face, iris and finger print

features are too fused to identify a person [27].

1.3.2 Test Section

In this section, the one sample of biometric test image is considered to

authenticate a person. The pre-processing and feature extraction on test

image is similar to that of enrolment section.

1.3.3 Classification Section

The given test biometric data is compared with database biometric to

authenticate a person is discussed in this section. The distance formulae

such as Euclidean Distance (ED) [28], Hamming distance [29], Chi-

25

square [30] are used for comparisons. The classifiers such as Support

Vector Machine (SVM) [31], Artificial Neural Network (ANN) [32] and

Random Forest (RF) [33],[ 34],[ 35], Multiple Classifier Systems (MCS)

[36], Template Matching [37], Graph Matching [38] and Mahanalobis

Distance [39] are used for matching.

1.4 DESIGN ISSUES IN BIOMETRIC SYSTEM

The biometric traits can be used as biometric characteristics to

identify an individual as long as it satisfies the following parameters.

There are at least different biometric techniques commercially available

and new techniques are in the stage of research and development. Any

human physiological or behavioral characteristics can become a biometric

provided the following properties are fulfilled:

Universality: The human beings must have some common body parts

such as face, finger, palm, iris etc. It is really difficult to get 100%

coverage. There are mute people, people without fingers or with injured

eyes. A good biometric system should handle all the types of people

Collectability: The features of human body parts are acquired and

quantitatively measured. Face recognition systems are not intrusive and

obtaining of a face image is easy. In the contrast the DNA analysis

requires a blood or other bodily sample. The retina scan is rather intrusive

as well.

26

Distinctiveness: The patterns of each biometric trait of any two

persons in the world should be distinct and different in terms of both

physiological and behavioral characteristics.

Permanence: The physiological and behavioral characteristics of

biometric traits should not change over a period of time pertaining to

recognition criterion. While the iris usually remains stable over decades, a

person’s face changes significantly with time. The signature and its

dynamics may change as well and the finger is subject to injuries

Acceptability: In general people need to accept a particular biometric

identifier for day-to-day business or any related transactions.

Uniqueness: The biometric characteristic that differentiate effectively

between persons. Sometimes biometric traits like face recognition, DNS

etc., may be not useful for the identical twins

Circumvention: This refers to how difficult it is to fool the system by

fraudulent techniques. An automated access control system that can be

easily fooled with a fingerprint model or a picture of a user’s face does

not provide much security.

1.5 APPLICATIONS OF BIOMETRICS SYSTEM

The biometric need for security systems is going up, hence

recognition of human being every day based on fully automated personal

identification and authentication has been attracting extensively over the

27

past two decades. Some of the biometric system applications are as listed

below.

(i) The biometric systems have wide range of applications in different

areas such as human-computer interaction, image processing, film

processing, security applications, computer access control, criminal

screening and surveillance.

(ii) Banking systems

(iii) Regular attendance monitoring and authentication of the

employees using any of the biometric traits.

(iv) Airport checking for personal authentication

(v) Home security applications

(vi) Electronic voting system

(vii) Military force to authenticate refugee

(viii)Using a pre-stored image database, the biometric recognition system

is able to verify and authenticate one or more persons in the

database.

(ix) Biometric is one of the major research topic in the current fields such

as neural networks, man and machine intelligence system, robotics

and computational vision, computer graphics, image processing and

psychology study.

28

1.6 DEFINITIONS

(i) Pixels: Picture Element (pel) is a single point image and is

addressable as small screen element. The pixel can be controlled and is

represented using dots or squares with its own address. The address is

corresponds to its coordinates with a two dimensional grid.

(ii) Histogram: The graphical representation of digital image pixels by

showing the number of pixels at each intensity level found in the image is

defined as histogram of an image.

(iii) Histogram Equalization: The technique which enhances the

dynamic range of an image by assigning intensity values of pixels in the

input image is referred to as histogram equalization. The image obtained

after histogram equalization has pixels with uniform distribution of

intensities.

(iv) Threshold: The distance between the test image and the database

images are recorded as Error Vector (EV) using distance formula and then

the average of EV is considered as the threshold value for recognition

declaration.

(v) False Rejection Rate (FRR): The ratio of the number of false

rejections to the number of identification attempts and is given in

Equation 1.1.

)1.1(...ofno. Total

system theby ejectedofNo.FRR

attemptstionidentifica

rpersonsauthorised

29

(vi) False Acceptance Rate (FAR): The ratio of the number of false

acceptances to the number of identification attempts and is given in

Equation 1. 2.

(1.2)ofno. Total

accepted images test edunauthorizofNo.FAR .......

attemptstionidentifica

(vii) Equal Error Rate (EER): The rate at which both accept and reject

errors are equal and it is taken from the region of convergence plot by

considering the point where FAR and FRR have the same value. The

system with the least EER is most accurate.

(viii) Correct Recognition Rate (CRR): The CRR measures the

percentage of match rate regardless of the FRR and is given in the

Equation 1. 3.

1.3atabasetheinofno.Total

correctlyatches images testofNo.CRR ..............

dpersons

m

(ix) Fingerprint: Impression of a finger acquired by digital scanners.

(x) Minutiae: Ridge bifurcations, Ridge endings in fingerprint image.

(xi) Ridge bifurcation: The ridge splits into two ridges.

(xii) Ridge termination: The ridge end point.

(xiii) False Minutiae: The points which are incorrectly identified as

minutiae.

30

1.7 MOTIVATION

The biometric validation of a person have several advantages

compared to the earlier validation systems such as smart cards, PIN,

password, visiting cards, credit cards and debit cards. The biometric

identification is based on several parts of human body and behavioral

characteristics such as face, fingerprint, iris, retina, palm print, hand

geometry, DNA, voice and signature. Hence I have been motivated to

design and implement a robust fingerprint and face biometric system with

high recognition rate and low FRR and FAR for variations in the

biometric images

1.8 ORGANIZATION OF THE THESIS

The organization of the thesis is as follows. Chapter one presents a

detailed introduction to biometrics, biometric system, applications of

biometrics, design issues related to biometrics and motivation for the

research work. The detailed literature survey on existing fingerprint and

face recognition models using different techniques is presented in chapter

two. Transform domain fingerprint identification based on DTCWT is

described in chapter 3. Face recognition based on Transform Domain

Technique is given in chapter 4. Finally conclusions, contributions and

future work are presented in chapter five.

CHAPTER 2

LITERATURE SURVEY

31

CHAPTER 2

LITERATURE SURVEY

2.1 INTRODUCTION

In this chapter a review of the current literature on fingerprint and

face recognition are presented. It includes a review of different techniques

and algorithms used for authenticating and identifying a human being

using fingerprint and face biometrics.

2.2 REVIEW OF FINGERPRINT BIOMETRIC TRAIT

Michael Kucken and Newell [40] discussed the hypothesis on the

development of epidermal ridges viz., (i) The epidermal ridge pattern is

established as a result of buckling instability acting on the basal layer of

the epidermis and resulting in the primary ridges. (ii) The buckling

process underlying fingerprint development is controlled by the stresses

formed in the basal layer and not by the curvatures of the skin surface and

(iii) the stresses that determine ridge direction are themselves determined

by boundary forces acting at creases and nail furrow, normal

displacements which are most pronounced close to the ridge. Shlomo

Greenberg et. al., [ 41 ] proposed two methods for fingerprint image

enhancement. The first one is carried out using local histogram

32

equalization, Wiener filtering and image binarization. The second

method is unique anisotropic filter for direct grayscale enhancement.

Bazen and Gerez [42] presented methods for the estimation of a high

resolution directional field from fingerprints. The directional field detects

the singular points and the orientations of the points. Yun and Cho [43]

proposed an adaptive preprocessing method, which extracts five features

from the fingerprint images, analyzes image quality with clustering

method, and enhances the images according to their characteristics. The

preprocessing is performed after distinguishing the fingerprint image

quality according to its characteristics.

Brankica Popovi´c and Maskovic [44] used multiscale directional

information obtained from orientation field image to filter the spurious

minutiae. The feature extraction in pattern recognition system is to extract

information from the input data and depends greatly on the quality of the

images. Multiscale directional information estimated based on orientation

field estimation. Afsar et. al., [ 45 ] presented the minutiae based

Automatic Fingerprint Identification Systems. The technique is based on

the extraction of minutiae from the thinned, binarized and segmented

version of a fingerprint image. The system uses fingerprint classification

for indexing during fingerprint matching. Jagadeeswar Reddy et. al., [46]

presented fingerprint denoising using both wavelet and Curvelet

Transforms. The search-rearrangement method performs better than

33

minutiae based matching for fingerprint binary constraint graph matching

since it does not require implicit alignment of two fingerprint images.

Zebbiche and Khelifi [47] presented biometric images as one Region

of Interest (ROI). The scheme consists of embedding the watermark into

ROI in fingerprint images. Discrete Wavelet Transform and Discrete

Fourier Transform are used for the proposed algorithm. Bhupesh Gour et.

al., [48] introduced midpoint ridge contour representation in order to

extract the minutiae from fingerprint images. Colour coding scheme is

used to scan each ridge only once. Seung Hoonchae and Jong Ku Kim

[49] proposed Fingerprint Verification in which both minutiae and ridge

information are used to reduce the errors due to incomplete alignment or

distortion.

Aparecido Nilcau Marana and Jain [ 50 ] proposed Ridge Based

Fingerprint matching using the Hough transform. The major straight lines

that match the fingerprint ridges are used to estimate rotation and

translation parameters. Anil Jain et. al., [51] described the use of logistic

regression method to integrate multiple fingerprint matching algorithms.

The integration of Hough transform based matching, string distance based

matching and 2D dynamic programming based matching using the

logistic regression has minimized the False Rejection Rate for a specified

level of False Acceptance Ratio. Fanglin Chen and Jie Zhou [ 52 ]

proposed an algorithm for reconstructing fingerprint orientation field

34

from saved minutiae and are used in the matching stage to compare with

the minutiae from the query fingerprint. The orientation fields computed

from the saved minutiae is a global feature and the saved minutia is the

local feature, are used to get more information.

Chunxian Ren and Yilong Yin [53] used the hybrid algorithm based

on linear classifier to segregate foreground and background blocks. The

pixel wise classifier uses three pixel features such as Coherence, mean

and variance. Hartwig Front haler and Klaus Kollreider [ 54 ] used a

multigrid representation of a discrete differential scale space enhancement

strategy of fingerprint recognition system. The fingerprint image is

decomposed using Laplacian Pyramid as relevant information is

concentrated within a few frequency bands. The Fausian Directional

Filtering is used to enhance ridge valley pattern of fingerprint using 1-D

filtering on higher pyramid level. The linear symmetric features are used

to extract the local ridge –valley orientation. Chaohong Wu and Sergey

Tulyakov [ 55 ] proposed Harris corner point based fingerprint

segmentation method which strongly discriminates between foreground

and background features of the fingerprint. Liu Wei and Zhou Cong [56]

proposed Gradual Segmentation algorithm and multi segmentation

features for finger print image segmentation. The fingerprint region is

obtained using a Gradual Segmentation and recoverable region are

segmented using Multi Segmentation feature algorithm.

35

Hemanth Krishnappa and Hongyu Guo [57] proposed the algorithm of

fingerprint verification using mutual information. The input image is

subjected to preprocessing using filtering and normalization which

involves translation and Rotation. The mutual information at each step of

Rotation and translation was calculated to find the best alignment of

fingerprint by maximizing the mutual information. Swapnali Mahadik et.

al., [58] described an Alignment based Minutiae Matching algorithm. The

minutiae extraction involves Filtering, Binarization, Orientation

Estimation, Region of interest, Thinning and Minutiae Extraction. In the

matching stage the images are subjected to translation Rotation and

Scaling.

Yi Chen and Anil K Jain [ 59 ] proposed an algorithm based on

fingerprint features viz., minutiae and ridges, Pattern and Pores. The

correlation among Fingerprint features and their distributions are

considered for the model. Johg Ku Kum et. al., [60] presented a study on

Hybrid fingerprint matching methods. The minutiae and image based

fingerprints verification methods are implemented together. The shapes in

the fingerprint such as square, diamond, cross and dispersed cross are

used for matching. Manvjeeth Kaur et. al., [61] proposed fingerprint

verification system adopting many methods to build a minutiae extractor

and a minutiae matcher. The method with some changes like

segmentation using morphological operation, improved thinning,

36

minutiae marking with special Triple Branch Counting, Minutiae

unification by decomposing a branch into three terminations, matching

the unified x – y co-ordinate system are employed. Liu Wei [ 62 ]

described Rapid Singularity Searching for fingerprint classification. The

algorithm uses Delta Poincare Index and Rapid Classification algorithm

to classify the fingerprint into five classes. The Singularity is achieved by

Detection algorithm which searches the direction field that has the larger

directional change. Arun Ross et. al., [63] proposed the hybrid fingerprint

matcher which employs the combination of ridge strengths and a set of

minutiae points.

Haiyun Xu et. al., [64] introduce two feature reduction algorithms: the

Column Principal Component Analysis and the Line Discrete Fourier

Transform feature reductions, these algorithms can be efficiently

compress the template size with rate of 94%. Also they present the

performance of the spectral minutiae fingerprint recognition system and

show a matching speed with 125000 comparisons per second on a PC

with Intel Pentium D processor 2.80 GHz and 1 GB of RAM. Jian-wei du

et. al., [65] proposes the fractal features for glycyrrhiza fingerprint of

medicinal herbs EMD to obtain the IMF from high to low frequency.

After this EMD fractal features in fingerprint of medicinal herbs are

extracted through computing the fractal dimensions of every IMF. For the

recognition of glycyrrhiza fingerprints of medicinal herbs, novel approach

37

is applied. Xiao Xiaohong and Niu Jiping [ 66 ] describes about the

principle of EMD and different types of its extensions such as BEMD,

CEMD, and EEMD. They also give the application in fusing the image.

The advantages and the difficulties while fusing the image are described.

Li Lin and Ji Hongbing [67] use an improved EMD method for signal

feature extraction. The optimal envelopes mean is obtained by an inverse

EMD filter scheme in this method. A new sifting stop criterion is

proposed to guarantee the orthogonality of the sifting results. Numerical

simulation and experimental result demonstrate the validity of the

improved method.

Tachaphetpiboont and Amornraksa [68] proposes a feature extraction

method based on FFT for the fingerprint matching. The recognition rate

obtained from the proposed method is also evaluated by the k- NN

classifier. The amount of time required for the extraction and verification

is very less in this approach. Kemingmao et. al., [ 69 ] proposed a

fingerprint image segmentation method aiming at dealing with low

quality fingerprint images using the two new features, intra-consistency

and extra consistency. In the segmentation stage, the frequency and

orientation of local ridge are obtained. Intra-consistency, which used to

further partition and extra consistency, used to reflect the relationship

between centre block and its neighbour blocks, and is used to validate the

shadow and the boundary area between foreground and background.

38

Gabor filter method is utilized to reduce the counting cost. Baiget et. al.,

[70] proposed a robust segmentation algorithm which uses the strength of

Harros corners for segmentation. The algorithm employs dynamic

thresholding and simple binary operations to provide highly accurate

segmentation of the image. This algorithm provides very accurate

segmentation even for low quality images. Furthermore, the algorithm is

also effective in segmenting corrupt areas within the fingerprint images.

Aayush Sharma et. al., [71] proposed an approaches for performance

optimization is to fuse two or more biometric matcher technologies to add

on to the performance of the individual systems. Here the author present

new step integration based fusion method for multimodal biometric

technologies, based upon eliminative machine learning. The individual

matcher systems are integrated in steps eliminating unwanted user classes

at each matcher step. The method achieves high accuracies and

recognition rates, achieving low processing times at the same time.

Teoh and Pang [72] proposed a touch-less fingerprint recognition

system by using a digital camera. They proposes a pre-processing

technique which comprised of low passing filtering, segmentation and

Gabor enhancement for their own-designed touch less sensor. The feature

extraction is done by Gabor filter and the favourable verification results

are attained with the Support Vector Machine.

39

Tiande Gue et. al., [73] proposed a novel segmentation approach

which is very different from the traditional segmentation methods. This

method is based on the local binary pattern (LBP) which is having a good

performance on texture discrimination. It uses LBP operator to transform

the image signal and analyzes the histogram of uniform LBP information

to decide the corresponding image block whether to be segmented or not.

The most attractive advantages of LBP are its invariance to monotonic

gray scale changes, low computational complexity and convenient muti-

scale extension.

Chen Yu et. al., [74] proposed a system to authenticate the persons in

online examination system for large scale. They presented a novel

principal component analysis neural network algorithm for fingerprint

recognition. The algorithm to meet the convergence conditions and to

simplify the complex pre-processing steps, greatly reducing the

computation to improve the recognition speed. The algorithm used here is

to obtain a higher recognition rate for fingerprint examination online

algorithm provides new and effective methods.

Conti et. al., [75] proposed an interface for the use and management

of biometric recognition systems. Here they place before an interface

which allows for an intuitive parameterization of functions and

procedures of system algorithms, for the optimized management of great

database by unspecialized operators. It is based on an ad-hoc language, a

40

subset of natural language that makes possible all the functionalities

offered by a biometric recognition system. It also allows a

parameterization of functions and procedures of system algorithms for an

better management of large databases by specialized users. Raffaele

Cappelliet et. al., [76] studied on the spatial distributions of singularity

locations in nature and derives, from a representative data set of labeled

samples, the probability density functions of the four main fingerprint

classes. The singularity positions have been aligned with respect to

fingerprint centered and scaled according to the average width.

Chunfeng Hu et. al., [77] proposed fingerprint alignment using special

ridges. The ridge with the maximum of sampled curvature is used for

initial alignment and other corresponding ridges then align using different

features. The speed of authentication depends on the algorithm used for

the alignment. Macro details of whole image, Global and those of a point,

ridge or block, local are the two characteristics in fingerprint

authentication method. Comparing to the local features, global features

are fast but not rebuts. In global features, there can be some missing local

features which will affect the accuracy of the alignment. In the fingerprint

verification minutiae features are more important, since it contain most of

fingerprint individuality. Dadgostaret et. al., [ 78 ] employed wavelet

based features for fingerprint recognition. The proposed method is

assessed on image from the biodata database and has lower computational

41

complexity and higher accuracy rates than conventional methods on

texture features. This method has both frequency – selective and

orientation – selective properties and have optimal joint resolution in both

spatial and frequency domains. So Gabor filters are used to remove noise

and preserve true ridge structures.

Young Li et. al., [79] proposed two algorithms based on match scores

namely; maximum match scores and greedy maximum match scores.

These algorithms are useful and more flexible and can be used in various

biometric systems. Registration and authentication are the two stages

involved in the biometric systems. In registration, multiple samples of the

same biometric traits will be captured. Xiaohui Renet et. al., [ 80 ]

implements multi-fingerprint fusion bases on D-S evidence theory with an

improved D-S combination rule. They combined fingerprint information

together for personal identification. This method is to improve the

traditional D-SCR. Experimental results can effectively improve the

correct recognition rate of the multi-fingerprint identification system. Anil

Jain et. al., [81] proposes a hierarchical matching system that utilizes

features at all the three levels extracted from 1,000 ppi fingerprint scans.

Level 3 features, including pores and ridges contours, are automatically

extracted using Gabor filters and wavelet transform and are locally

matched using the iterative Closest Point (ICP) algorithm. This

experiment shows that Level 3 features carry significant discriminatory

42

information. There is a relative reduction of 20 percent in the equal error

rate (EER) of the matching system when Level 3 features are employed in

combination with Level 1 and Level 2 features. The experimental results

demonstrate that Level 3 features should be examined to refine the

establishment of minutia correspondences provided at level 2. And also,

consistent performance gains were also observed in both high quality and

low quality images, suggesting that automatically extracted Level 3

features can be informative and robust.

Sunny Arief Sundiroet et. al., [82] proposes the technique to enhance

the process to get the good quality fingerprint images to produce good

results. This enhancement process in simple minutiae detection algorithm

using crossing number on valley structure improves detection of true

minutiae. In the experiment, this process reduces the number of average

minutiae points from 854 to 59 minutiae and leaves the good minutiae

points even though the computation time increases 2.5 times. And in

order to speed up the computation time, the author intends to implement

the algorithm on embedded hardware system using FPGA device.

Raffaele Cappelli et. al., [83] proposed a novel approach to reconstruct

fingerprint images from standard templates and investigates to what

extent the reconstructed images are similar to the original ones. The

proposed reconstruction approach could be further improved to make the

success probability of a masquerade attack even higher. Here the authors

43

encourage developers of algorithms and systems to seriously take into

account this kind of attack and to implement specific protections and

counter measures. Thi Hoi Le et. al., [ 84 ] proposed an approach to

improve the performance of fingerprint indexing process. Moreover, this

technique itself provides privacy property for fingerprint system which is

not mentioned in previous indexing techniques. A fingerprint indexing

algorithm selects most likely fingerprint candidates and sorts them by the

similarity to the query fingerprint template.

Seong – Jin Kim et. al., [85] proposed a 200 X 160 pixel CMOS

fingerprint System – On- Chip (SOC) with a local adaptive pixel scheme

and embedded column – parallel processors for performing 2 – D digital

image processing for fingerprint recognition. The SOC can generate

robust fingerprint images enhanced by a local adaptive scheme and on –

chip signal processing in both analogue and digital domains. The on-chip

self-configurable column – parallel image processors can provide

adaptive filter operations in a small form factor by leveraging the

advantages of parallelism and flexibility. Liu Wenzhou et. al., [ 86 ]

proposed a method for extracting fingerprint minutiae. After acquiring the

binary image, the method is applied in fingerprint verification system

about examinee, whose principle and basic structure, software and

hardware design are discussed. They introduced a method that thin the

ridges and valleys point when the fingerprint feature point was extracted

44

and 8 fields fingerprint trail algorithm was used to extract fingerprint

feature point concretely. Faisal Farooq et. al., [87] proposed a method

over the lack of anonymous and revocability of biometric templates. They

introduced binary string representations of fingerprints that obviate the

need for registration and can be directly matched. The given

representation is computationally infeasible to invert it to the original

fingerprint. The method indeed has the properties of verifiability and

revocability. We see the securing of the template by hashing using any

existing technique like MD5. Baig et. al., [ 88 ] proposes a robust

segmentation algorithm which uses the strength of Harros corners for

segmentation. The algorithm employs dynamic thresholding and simple

binary operations to provide highly accurate segmentation of the image.

This algorithm provides very accurate segmentation even for low quality

images. Furthermore, the algorithm is also effective in segmenting corrupt

areas within the fingerprint images.

Nocolas Galy et. al., [ 89 ] proposes a full fingerprint verification

system which is composes of a tactile fingerprint sensor, integrated read

out and conversion circuits, and dedicated recognition algorithms. The

use of a single line to measure a v requires the user to sweep its finger

along the sensor. This sensing scheme produces fingerprint image with

several distortions that needs further image processing to allow efficient

fingerprint recognition. To get ride of the distortion, a bank of directional

45

Log – Gabor masks have been used and new distortion model has been

implemented. Performance evaluation has been encouraging results and

has shown that some improvement have to be made regarding the quality

of images produces by the sensor. Falguera et. al., [90] proposes the

fusion of a minutiae-based and a ridge –bases fingerprint recognition

method at rank, decision and score level. They emphasized that the

combination of methods that use different kinds of information could be

an interesting research area. The authors intend to evaluate the fusion

technique in a database which images were obtained by fingerprint sensor

with a small sensing area. Here also testing the fusion using other

minutiae – based method.

Lopez et. al., [91] proposes the implementation of a whole minutiae

extraction fingerprint algorithm using a Spartan – 3 FPGA, as an

appropriate solution for portable devices and for the low cost consume

market. The experimental results show as minutiae of fingerprint are

obtained in 988 ms when an image of 256 X 256 pixels is analyssed. Here

they presents a hardware – software co-design. Daniel Ashlock et. al.,

[92] proposes the evolution of strategies for playing the iterated prisoner’s

dilemma (IPD) at three different noise levels is analyzed using

fingerprinting and other techniques including a novel quantity,

evolutionary velocity, derived from fingerprinting. Here, the study is on

substantially lengthened version of the experiments were rerun to permit

46

the gathering of new types of data and several additional analysis were

performed. A new quantity, evolutionary velocity, is introduced in this

study and is used to expose an unexpected and critical difference between

agents evolved with and without noise using the finite-state

representation. This paper also highlights two distinct forms of

competitive ability, the ability to dominate an evolving population and the

ability to win a contest against a diverse selection of opponents. These

two abilities, while not opposites, are demonstrated in this paper to have

some degree of trade off.

Dalwon Jang et. al., [93] proposes a method for learning a distance

metric in a fingerprinting system which identifies a query content by

measuring the distance between its fingerprint and a fingerprint stored in

a database. By learning a distance metric from training data consisting of

original and distorted contents, the identification performance can be

improved. Fingerprints of original contents are assumed to be fingerprints

stored in a DB, and fingerprints of distorted contents are assumed to be

the query fingerprints. For correct identification, the distance of the

fingerprint of a distorted content to the fingerprint of the original content

from which the distorted content was obtained and a large distance

margin should be established between fingerprints of the distorted and

non-corresponding contents. In this paper it is shown that the distance

metric learning improves the fingerprinting performance. Woo Kyu Lee

47

et. al., [ 94 ] proposes a fingerprint recognition algorithm which is

developed based on the wavelet transform, and the dominant local

orientation which is derived from the coherence and the gradient of

Gaussian. By using the wavelet transform, the algorithm does not require

conventional preprocessing procedures such as smoothing, binarization,

thinning and restoration. Their study have shown that while the rate of

Type I1 error is held at O.O%, the rate of Type I error turns out as 2.5%

in real time.

Xiangping Meng et. al., [95] proposes a kind of effective fingerprint

recognition algorithm composed of fingerprint images per treatment,

fingerprint character points extract and fingerprint match. Here the

acquired binary image thinned the ridges and valleys respectively, then

extracted ridge bifurcations and valley bifurcations as the key minutiae

from the ridge thinned image and valley thinned image. The ridge line

and valley line branch point was used to match fingerprint in the

matching process. In the fingerprint match process, Xiangping Meng et.

al., uses the coordinate transformation method that transformed the

unmatched detail point and the template detail point to the same

coordinate, then calculate the match score between them, finally may

obtain two fingerprints whether to come from the identical finger or not.

Jiao Ruili et. al., [96] proposes an automatic fingerprint acquisition and

preprocessing system with a fixed point DSP, TMS320VC5509A and a

48

fingerprint sensor, MBF200. The system is diminutive and flexible. The

authors presents a VC5509A based fingerprint preprocessing system,

accomplished fingerprint image acquisition. The preprocessing system is

accomplished with the properly selected algorithm on a DSP platform.

Comparing the results of the algorithms, appropriate algorithms are

selected for fingerprint identification preprocessing. They are Median

Filtering, Directional Filtering Enhancement, Fixed Threshold

Binarization, and Hilditch Thinning. Han Xi et. al., [ 97 ] proposes a

universal laboratory management system, which contains checking on

work attendance, student’s information management, querying and course

management functions. By using this technique, we are able to record the

right time and times of every student enter or leave the lab, afford the

opportunity to choose or modify the courses, query the students’

information related to the experiment content, times and time length.

Using this system, the situation of exceptional students imitate others to

join the experiment will be avoided; the ratio of laboratory use and

equipment use will be also confirmed. Utilizing the fingerprint

recognition technology, choosing the student’s fingerprint as the only

identify symbol to use the lab, the reliability of the recognition is

enhanced extremely, and at the same time of guaranteeing the normal

management order of the lab, the experiment interest of the students and

the passion of the lab assistants are stimulated greatly.

49

2.3 REVIEW OF FACE BIOMETRIC RECOGNITION

Jeffery and Masatoshi [98] proposed a new data structure known as

Haar Spectral Diagram (HSD) which is useful for representing the Haar

spectrum of boolean functions. To represent the Haar transform matrix in

terms of a Kro-necker product yielding a natural decision diagram based

representation is an alternative ordering of Haar coefficients. The

resulting graph is a point- decomposition of the Haar spectrum using O-

element edge values. Kun Ma and Xiaoou Tang [ 99 ] proposed an

algorithm by using discrete wavelet face graph. This graph is similar to

the Gabor face graph. They used the method of elastic bunch graph

matching process to locate fiducial points. They used 2340 face images

to compare the recognition performance of the two methods. As a result,

they conclude that DWT face graph has comparable performance as the

Gabor face graph.

Duan and Zheng [100] proposed a concept of gray-like image from

which generalized Haar like features can be exacted. The process make

use of other forms of images in addition to gray level image in Haar-

Adaboost schema. The application of the gray-like images, the

generalized Haar-like features are constructed for fast face detection. The

results show that the boosted face detector using the generalized Haar-

like features out performs significantly the original using the basic Haar-

like features. Paul and Abbes [101] proposed a method to determine the

50

most discriminative coefficients in a DWT/PCA-based face recognition

system. This is achieved based on their inter-class and intra-class

standard deviations. Also the eigen faces used for recognition are

generally chosen based on the value of their associated eigenvalues. Jun

Ying Gan and Jun Feng Liu [102] described a novel approach to the

fusion and recognition of face and iris image based on wavelet features.

They developed Kernel Fisher Discriminant Analysis (KFDA). In the

algorithm, after the dimension is reduced, the noise is eliminated and the

storage space is saved and then the efficiency is improved by Discrete

Wavelet Transform (DWT) to face and iris image. Also the face and iris

features are extracted by the fusion of KFDA. After the extraction,

nearest neighbor classifier is selected to perform recognition.

Experimental results shows that not only the small sample problem is

overcome by KFDA, but also the correct recognition rate is higher than

that of face recognition and iris recognition.

Sudha and Mohan [103] proposed a hardware oriented algorithm for

eigenface based face detection using FFT. Eigenfaces have long been

used for face detection and recognition and are known to give reasonably

good results. They have given the FFT-based computation of distance

measure which facilitates hardware implementation and fast face

detection. Also extended the face detection framework by training with

the whole face as well as other facial features like eyes, mouth etc.,

51

separately. Satiyan et. al., [ 104 ] investigated the performance of a

Daubechies Wavelet family in recognizing facial expressions. A set of

luminance stickers were fixed on subject’s face and the subject is

instructed to perform required facial expressions. Also the subject’s

expressions are recorded in video. A set of 2D coordinate values are

obtained by tracking the movements of the stickers in video using

tracking software. Standard deviation is derived from wavelet

approximation coefficients for each daubechies wavelet orders.

Hengliang Tang et. al., [105] proposed a novel face representation

approach known as Haar Local Binary Pattern Histogram (HLBPH). The

face image is decomposed into four-channel sub images in frequency

domain by Haar wavelet transform, and then the LBP operator is applied

on each sub image to extract the face features. Hafiz Imtiaz and Shaikh

Anowarul Fattah [106] proposed a multi-resolution feature extraction

algorithm for face recognition based on 2D-DWT. For feature

extraction, an entropy-based local band selection criterion is developed.

A very high degree of recognition accuracy is achieved by the proposed

method. Ramesh and Raja [107] proposed a performance evaluation of

face recognition based on DWT and DT-CWT using Multi-matching

Classifiers. The face images is resized to required size for DT-CWT.

The two level DWT is applied on face images to generate four subbands.

Euclidian Distance, Random Forest and Support Vector Machine

52

matching algorithms are used for matching. Masashi Nishiyama and

Osamu Yamaguchi [108] have proposed a method for synthesizing an

illumination on normalized image with diffuse reflection, specular

reflection, attached shadow and cast shadow using the Self-Quotient

Image i.e., the ratio of the pixel value to a locally smoothed pixel value.

Kekre et. al., [109] proposed a method of Novel Walshlets Pyramid

based face recognition technique for face recognition on two image

databases using 100 images each. The feature sets of image are extracted

from Walshlets applied on the image at different levels on gray plane for

improving the performance. Kailash Karande and Sanjay Talbar [110]

have developed a method for face recognition using edge information,

Laplacian of Gaussian and Canny edge detection techniques to generate

edge information and preprocessing is carried out by distance classifiers

for testing of images.

Mohamed Aroussi et. al., [111] have proposed a method for face

recognition based on steerable pyramid feature extraction. Local

information is extracted from SP sub-bands using Local binary Pattern

which make it to be more effective technique for face recognition.

Sanqiang Zhao and Yongsheng Gao [ 112 ] proposed a method on

Multidirectional Binary Pattern (MBP) for face recognition. MBP

algorithm is used on a sparse set of shape-driven points to extracting

more discriminative features. MBP measurement is proposed to evaluate

53

binary patterns and establish point correspondence for face recognition.

Rerkchai Fooprateepsiri and Werasak Kurutach [113] have proposed a

robust method for face recognition with variant illumination, scaling and

rotation. The features are extracted by combining the Trace Transform

and Fast Active Contour. To determine the similarity between models

and test images Hausdorff distance and Modified Shape Context are

used. The experimental results give that the average of accuracy rate of

face recognition with variant illumination, scaling and rotation.

Vitomir Struc and Nikola Pavesic [114] presents a novel method for

facial features extraction based on the Gabor-based kernel partial-least-

squares discrimination method. Gabor wavelets are applied to extract

discriminative and robust facial features and then the kernel partial-least-

squares discrimination method is used to reduce the dimensionality of the

Gabor feature vector. Further enhance its discriminatory power and the

cosine distance measure was employed at the matching stage for the

calculation of the matching scores to achieve the better results. Taskeed

Jabid et. al., [115] proposed a method based on feature descriptor and

local directional pattern to represent facial geometry for analyzing its

performance. LDP features are obtained by computing the edge response

values in 8 directions at each pixel and encoding them into an 8 bit

binary number using the relative strength of these edge responses. Arif

Muntasa et. al., [ 116 ] have proposed a model for Face Image

54

Recognition, global structure features are extracted using Principal

component analysis and linear descriminant analysis method and locality

preserving projection and orthogonal laplacian faces methods are used to

extraction the local structure features then they are fused the global and

the local structure features based on appearance to achieved higher

recognition rate.

Kelsey Ramirez-Gutierrez et. al., [117] have developed an algorithm

for face recognition using support vector machine and histogram

equalization techniques. Principal components analysis and histogram

equalization is used to extract features. This technique provides a

recognition rate higher. Jaya Priya and Rajesh [118] proposed a method

of novel local appearance feature extraction method using multi-

resolution Dual Tree Complex Wavelet Transform (DTCWT) to generate

coefficients to characterize the face texture. The recognition is done on

the basis of Euclidean Distance (ED) measure on block based feature

vectors. The proposed method gives better performs better and low

computational complexity. Reza Ebrahimpour et. al., [ 119 ] have

proposed a method on two-dimensional Expression-Independent face

recognition method based on features inspired by the human’s visual

ventral stream. A feature set is extracted which contains illumination and

view invariant C2 features from all images in the dataset. Then, these C2

feature vectors which derived from a cortex-like mechanism passed to a

55

standard Nearest Neighbor classifier to maintain the higher recognition

rate. John Adedapo and Adeniran [ 120 ] proposed an algorithm for

recognition and verification with one sample image per class, features

are extracted using two dimensional discrete wavelet transform (2D

DWT) from images and hidden Markov model (HMM) is used for

training, recognition. 90% correct classification (Hit) and False

Aacceptance Rate (FAR) of 0.02% was achieved. Timo Ojala et. al.,

[121] presents a very simple, efficient, multi resolution approach to gray-

scale and rotation invariant texture classification based on local binary

patterns and nonparametric discrimination of sample and prototype

distributions. This approach is very robust in terms of gray-scale

variations and invariant against any monotonic transformation of the gray

scale. Another advantage is computational simplicity as the operator can

be realized with a few operations in a small neighborhood and a lookup

table.

Ramesh et. al., [122] proposed the template based Mole Detection for

Face Recognition in which the person is identified by the presence of

mole on the face. In this, homomorphic filtering is used for illumination

compensation. Normalized Cross Correlation (NCC) matching is used to

detect intensity value and position of the mole with respect to the NCC

threshold values. Validation is done by comparing the value of mole with

grab out segmented image.

56

Murugan et. al., [123] have proposed the performance evaluation of

face recognition using Gabor filter, Log Gabor filter and discrete wavelet

transform. The PCA which is used to reduce the dimensionality of the

face images reduces the important information needed for matching.

Hence in this three multiscale techniques such as Gabor filter, log Gabor

filter and DWT are applied before PCA to preserve the important

information. Gabor filter is used to exploit salient visual properties such

as spatial localization, orientation selectively and spatial frequency

characteristics. It works a BPF to achieve optional resolution in both

spatial and frequency domain. In Log Gabor filter, log Gabor frequency

result is multiplied with Fourier transform of original image. Then

inverse Fourier transform of multiplied in high amplitude signal and less

prominent information appears in low amplitude. The image is

decomposed into 4 sub images Low-Low, Low-High, High-Low,High-

High by applying DWT along rows and columns. DWT gives high

compression ratio and good quality of reconstruction.

Ramesh et. al., [124] have proposed Dual Transform based Feature

Extraction for Face Recognition is proposed. The images from database

are of different size and format, and hence are to be converted into

standard dimension, which is appropriate for applying DT-CWT.

Variation due to expression and illumination are compensated by

applying DWT on an image and also DWT reduces image dimension by

57

decomposition. The DT-CWT is applied on LL subband, which is

generated after two-level DWT, to generate DT-CWT coefficients to

form feature vectors. The feature vectors of database and test face are

compared using Random Forest, Euclidian Distance and Support Vector

Machine matching algorithms.

Nick G. Kingsbury et. al., [125] proposed The dual-tree C WT is a

valuable enhancement of the traditional real wavelet transform that is

nearly shift invariant and, in higher dimensions, directionally selective.

Since the real and imaginary parts of the dual-tree C WT are, in fact,

conventional real wavelet transforms the C WT benefits from the vast

theoretical, practical, and computational resources that have been

developed for the standard DWT. Baochang Zhang et. al., [ 126 ]

introduced the establishment of polyu near infrared face database

(POLYU-NIRFED), which is one of the largest NIR based face

recognition method, namely directional binary code (DBC), was also

proposed to capture more efficiently the directional edge information in

NIR face images.

Haihong Zhang and Yan Guo [127] have proposed a method based on

Facial Motion Graph (FMG) for Facial Expression Recognition

(FER).which is based on feature points and muscle movements. FER is

achieved by analysing the similarity between unknown expressions.

Furthermore they propose a method to evaluate edge weights in FMG

58

similarity calculation to achieve a more accurate and robust system.

Praseeda Lekshmi and Sasikumar [128] have developed a method for

extracting face regions from the detected skin regions and facial

expressions are analyzed from facial images by using Gabor wavelet

transform (GWT) and Discrete Cosine Transform (DCT) and Radial

Basis Function (RBF) Network is used to identify the person.

Gandhe et. al., [129] have developed a contour matching based face

recognition system which uses “contour” for identification of faces. The

input contour is matched with registered contour using matching

algorithms to increase the high recognition rate. Dakshina Ranjan Kisku

et. al., [130] have proposed a model for Linear Discriminant Analysis for

face recognition to multi view faces, the Gabor filter bank is used to

extract facial features characterized by spatial frequency, spatial locality

and orientation. Canonical covariate is used to Gabor faces to reduce the

high dimensional feature spaces into low dimensional subspaces. Support

vector machines are trained with canonical sub-spaces contained a set of

features to perform recognition task. The experiment results demonstrate

the efficiency and robustness of the system with high recognition rates.

59

2. 4 SUMMARY

In this chapter a brief summary of the related works in the area of

biometrics is discussed. Literature survey of fingerprint recognition and

iris recognition are discussed in detail. Also various algorithms of these

biometric traits like development of epidermal ridges, image

enhancement, ridge based fingerprint matching using the Hough

transform, algorithms based on EMD, DWT, match score, rapid haar

wavelet decomposition etc. are discussed.

CHAPTER 3



60

CHAPTER 3



3.1 INTRODUCTION

In this chapter, Transform Domain Fingerprint Identification Based

on DTCWT is discussed. The physiological biometric characteristics are

better compared to behavioural biometric identification of human beings

to identify a person. Fingerprint recognition products accounted for high

percentage of the total sales of biometric technology. For the Transform

Domain Fingerprint Identification Based on DTCWT, The original

Fingerprint is cropped and resized to suitable dimension to apply

DTCWT. The DTCWT is applied on Fingerprint to generate coefficient

which form features. The performance analysis is discussed with different

levels of DTCWT and also with different sizes of Fingerprint database. It

is observed that the recognition rate is better in the case of level 7

compared to other levels of DTCWT.

3.2 PROPOSED MODEL

The block diagram of Transform Domain Fingerprint Identification

using DTCWT (TDFID ) is given in the Figure 3.1.

61

Fig. 3.1: TDFID Model

3.2.1 Fingerprint Database

The first and second international Competitions on fingerprint

verification (FVC2000 and FVC 2002) were organized in 2000 and 2002

respectively. The FVC 2004 competition [131] is mainly based on the

fingerprint verification. The Database is collected with various sensors

and different timings are provided by the competition organizers.

A total of ninety students (24 years old on the average) enrolled in the

Computer Science degree program at the University of Bologna

Test Fingerprint

Database

MATCHING

DTCWT

DTCWT

Fingerprint

Database

Preprocessing

Preprocessing

ACCEPT / REJECT

62

volunteers were randomly made into three groups of 30 persons; each

group of 30 person were associated to a DB in order to use different types

of fingerprint scanner;

Each volunteer was invited with at least two weeks’ time to present

him/herself at the collection place in three distinct sessions.

Forefinger and middle finger of both the hands (four fingers total) of

each person were acquired by interleaving the acquisition of the different

fingers to maximize differences in finger placement;

Image quality was not taken into consideration. So the sensor platens

were not systematically cleaned.

At each session, four impressions were acquired of each of the four

fingers of each person;

During the second session, individuals were requested to lay it on

thick skin distortion of the finger; during the third session, fingers were

dried and moistened. At the end of the data collection, for each database a

total of 120 Fingers and 12 impressions per finger using 30 volunteers

were collected.

Four databases collected constitute the FVC2004 benchmark. The

details of collected fingerprints are shown in Table 3. 1. Four distinct

databases, DB1, DB2, DB3 and DB4 constitute the benchmark for FVC

2004. Each database is 110 fingers wide and 8 samples per finger in depth

(i.e., it consists of 880 fingerprint images).

63

Table 3.1: Scanners/Technologies used for FVC 2004 Database

Sensor Type

Image

Size

Set A

(wxd)

Set B

(wxd)

Resolution

DB1 Optical Sensor 640 X 480 100 X 8 10 X 8 500 dpi

DB2 Optical Sensor 640 X 480 100 X 8 10 X 8 500 dpi

DB3

Thermal

sweeping sensor

640 X 480 100 X 8 10 X 8 512 dpi

DB sFinge v3.0 640 X 480 100 X 8 10 X 8

About 500

dpi

Each database will be partitioned in two disjoint subsets A and B. The

subsets DB1-A, DB2-A, DB3-A and DB4-A, which contain the first 100

fingers (800 images) of DB1, DB2, DB3 and DB4, respectively, will be

used for the algorithm performance evaluation. The subsets DB1-B, DB2-

B, DB3-B and DB4-B, containing the last 10 fingers (80 images) of DB1,

DB2, DB3 and DB4, respectively, will be made available to the

participants to allow parameter tuning before executable(s) submission.

During performance evaluation, only homogeneous fingerprints, i.e.

those belonging to the same database, will be matched against each other.

The image format is TIF, 256 gray-level, uncompressed.

64

The image resolution, which could slightly change depending on the

database, is about 500 dpi.

The image size varies depending on the database.

The orientation of fingerprint is approximately in the range [-30,

+30] with respect to the vertical orientation.

Each pair of images of the same finger will have a non-null overlap,

but the presence of the fingerprint cores and deltas is not guaranteed.

The Figure 3.2 shows a sample images of each fingerprint from

databases DB1, DB2, DB3 and DB4. A sample of finger print with eight

impressions of DB3 A is shown in Figure 3.3.

Fig. 3.2: One Fingerprint Image From Each Database

Fig.3.3: A Sample of Fingerprint of DB3_A

(i) Source Database: The first seven Fingerprint images of each

person from DB3 _A database of FVC 2004 are stored ie., seven hundred

samples.

65

(ii) Test Template: The eighth Fingerprint of each person from DB3 _A

database of FVC 2004 are used in the test template and is compared with

source database to compute FRR and TSR.

(iii) Mismatch template database: The DB3_B of FVC 2004 database

of 10 fingers are stored in Mismatch template database and compared

with source database to compute FAR.

3.2.2 Pre-processing

The original Fingerprint image is of size 480 X 300. On observing the

DB3_A of FVC 2004, we crop the input image to the size of 401 X 201 in

order to remove the unwanted portion in the image. The cropped image is

resized into 512 X 256 for the DTCWT requirement.

3.2.3 Dual Tree Complex Wavelet Transform (DTCWT):

The DTCWT is a recent enhancement technique to the DWT with

some additional properties and changes. It is an effective method for

implementing an analytical wavelet transform, introduced by Kings bury

in 1998 [132, 133, 134]. DTCWT gives the complex transform of a signal

using two separate DWT decompositions ie., tree a and tree b. DTCWT

produces complex coefficients by using a dual tree of wavelet filters and

gives real and imaginary parts as shown in Figure 3.4.

66

Fig. 3.4: Real and Imaginary Parts of the Complex Coefficients

While applying the DTCWT in different levels, the number of

features and dimensions are reduces. The Fingerprint images for level-1,

level-2, level-3, level-4 and level-5, level-6, level-7 are shown in the

Figure. 3.5.

Fig. 3.5: DTCWT Images at Different Levels.

Level-2

Level-7 Level-5

Level-6

Level-3

Level-1

Level-4

67

3.2.4 Feature Extraction by DT-CWT

The feature representation should have information sufficient to

classify various faces and be less sensitive to noise. Only the significant

features of the face must be encoded so that comparisons between

templates can be made. The feature elements capture the local

information and the ordered sequence captures the invariant global

relationships among the local patterns. The feature representation should

have information enough to classify various faces and be less sensitive to

noise.

For many natural signals, the wavelet transform is a more effective

tool than the Fourier transform. The wavelet transform provides a multi

resolution representation using a set of analysing functions that are

dilations and translations of a few functions (wavelets). The wavelet

transform comes in several forms. The critically-sampled form of the

wavelet transform provides the most compact representation; however, it

has several limitations. For example, it lacks the shift-invariance property,

and in multiple dimensions it does a poor job of distinguishing

orientations, which is important in image processing. For these reasons, it

turns out that for some applications improvements can be obtained by

using an expansive wavelet transform in place of a critically-sampled one.

(An expansive transform is one that converts an N-point signal into M

coefficients with M > N.) There are several kinds of expansive DWTs;

68

here we describe and provide an implementations of the dual-tree

complex discrete wavelet transform (DTCWT).

3.2.4.1 The DTCWT has following properties:

(i) Approximate shift invariance;

(ii) Good directional selectivity in 2-dimensions (2-D) with Gabor-like

filters also true for higher dimensionality: m-D);

(iii) Perfect reconstruction (PR) using short linear-phase filters;

(iv) Limited redundancy: independent of the number of scales: 2:1 for

1-D ( 2m :1 for m-D);

(v) Efficient order-N computation - only.

DTCWT differentiates positive and negative frequencies and

generates six subbands oriented in ±15°, ±45°, ±75°.

3.2.4.2 DTCWT Applications

The applications including image segmentation [ 135 , 136 ],

classification [137], deconvolution [138, 139], image sharpening [140],

motion estimation [141], coding [142, 143, 144], watermarking [145,

146], texture analysis and synthesis [147, 148], feature extraction [149,

150 ], seismic imaging [ 151 ], and the extraction of evoked potential

responses in EEG signals [152].

69

3.2.5 Matching:

The Euclidean Distance (ED) is used to identify the test image with

the database images using Equation 3.1.

( ) √

∑ ( )

….. (3.1)

Where, M = the dimension of feature vector,

= is the database feature vector

= is the test feature vector.

3.3 ALGORITHM

The physiological trait Fingerprint is used to identify a person using

the features obtained by the coefficients of DTCWT. The proposed

algorithm for the performance analysis of the fingerprint identification for

different levels of DTCWT is given in Table 3.2.

The objectives are;

(i) Fingerprint verification to authenticate a person using DTCWT

(ii) To achieve high TSR

(iii) To have FRR and FAR very low

http://www.iciba.com/data%20processing/

70

Table 3.2 Proposed TDFID Algorithm.

Input : Fingerprint Database, Test Fingerprint

Output : Person is identified.

1. FVC 2004, DB3_A database is considered.

2. Pre-processing is done by cropping the input fingerprint image to size

401 X 201.

3. Cropped image is resized to 512 X 256 for DTCWT requirement.

4. DTCWT is applied on Fingerprint with levels 5, 6, 7.

5. Magnitude and phase resulted from DTCWT are concatenated and

Considered as features.

6. Source database is created with the features obtained by step 5.

7. For the test Fingerprint DTCWT is applied and features obtained using

step 5.

8. Test Fingerprint is compared with the database fingerprint using ED to

Identify a person

3.4 PERFORMANCE ANALYSIS

For the performance analysis, DB3_A of FVC 2004 Fingerprint

database is considered. The number of Persons Inside the DataBase

(PIDB) to compute FRR and TSR are varied from 30 to 90 and the

number of Persons Outside the DataBase (PODB) are 10 to compute FAR

is given in Table 3.3.

71

Table 3.3: EER and TSR for Different Levels of DTCWT

Levels

PIDB:PODB

30:10 40:10 60:10 70:10 80:10 90:10

5

EER 0.5 0.2 0.573 0.34 0.36 0.33

% TSR

50 80 42.7 66 64 67

6

EER 0.45 0.2 0.59 0.3 0.282 0.3

% TSR

55 80 41 70 71.8 70

7

EER 0.36 0.15 0.228 0.21 0.197 0.197

% TSR

64 85 77.2 79 80.3 82.1

72

It is observed from the Table 3.3 that the values of EER and TSR

depend on the quality of Fingerprint image than the number of images in

PIDB and PODB. The values of EER and TSR are better in the case of

PIDB: PODB of 40:10. The recognition rate is better in DTCWT level 7

compared to other lower levels of DTCWT. The TSR and EER is 85%

and 0.15 respectively for DTCWT level 7 with PIDB:PODB of 40:10.

The graph for FRR, FAR and TSR is given in Figure 3.6 and the

variations of FRR and TSR with threshold for POIB: PODB of 40:10 is

tabulated in Table 3.4 and It is noticed that as threshold increases, the

value of FRR decreases, whereas the values of FAR and TSR increases.

The highest success rate of recognition of 85% is achieved for the

threshold value of 2.4.

Fig.3.6 Variations of FRR, FAR and TSR with Threshold Values

73

Table 3.4. Values of FRR, FAR and TSR for Different Thresholds

Threshold FRR FAR % TSR

0 1 0 0

0.3 1 0 0

0.6 1 0 0

0.9 1 0 0

1.2 0.85 0 50

1.5 0.6 0 60

1.8 0.35 0 64

2.1 0.15 0.1 72

2.4 0.1 0.4 85

2.7 0.05 0.4 85

3 0 0.4 85

3.3 0 0.5 85

3.6 0 0.8 85

3.9 0 0.8 85

4.2 0 0.8 85

4.3 0 0.9 85

4.8 0 0.9 85

5.1 0 1 85

5.4 0 1 85

5.7 0 1 85

6 0 1 85

74

3.5 SUMMARY

The biometric is used to authenticate a person. The Performance

Analysis of Fingerprint Identification using different levels of DTCWT is

proposed in this chapter. The Fingerprint is preprocessed to a suitable size

that suit DTCWT. The Fingerprint features are obtained by applying

DTCWT with different levels. The test image features are compared with

Database images using Euclidean Distance. It is observed that the

recognition rate is better in the case of DTCWT level 7 compared to

lower levels with PIDB:PODB of 40:10.

CHAPTER 4

PERFORMANCE COMPARISON OF FACE

RECOGNITION USING TRANSFORM DOMAIN TECHNIQUES (PCFTD)

75

CHAPTER 4

PERFORMANCE COMPARISON OF FACE RECOGNITION

USING TRANSFORM DOMAIN TECHNIQUES (PCFTD)

4.1 INTRODUCTION

The biometrics is a powerful tool to authenticate a person for multiple

applications. The face recognition is better biometrics and recognizing

people by their facial features is the oldest identification mechanism

compared to other biometric traits as the image can be captured without

the knowledge and cooperation of a person. So face recognition got more

popularity among the people. Face recognition is a non-invasive process

where a portion of the subject’s face is photographed and is reduced to a

digital code for the further process. Facial recognition records the spatial

geometry of distinguishing features of the face. This chapter explains the

Performance Comparison of Face Recognition using Transform Domain

Techniques (PCFTD). The face databases L – Speack, JAFFE and NIR

are considered. The features of face are generated using wavelet families

such as Haar, Symelt and DB1 by considering approximation band only.

The face features are also generated using magnitudes of FFTs. The test

image features are compared with database features using Euclidian

Distance (ED). The performance parameters such as FAR, FRR, TSR and

EER computed using wavelet families and FFT.

76

4.2 PROPOSED PCFTD MODEL

In this section, the proposed model for the Performance Comparison

of Face Recognition using Transform Domain Techniques are discussed.

In the proposed model Haar, Symlet and DB1 of DWTs and FFT

transformations are applied to generate features of face images to identify

a person effectively. The block diagram of proposed model, PCFTD is

shown in the Figure 4. 1.

Fig. 4.1: The Block Diagram of PCFTD Model

Face Database Test Image

Preprocessing

DWT / FFT

Features

Preprocessing

DWT / FFT

Features

Matching

Result

77

4.2.1 Face Databases:

(i) Near Infrared (NIR)

The NIR data base is considered due to its variation of pose,

expression, illumination, scale, blurring and a combination of them. The

database consists of 120 persons with 15 images per person. The data

base is created by considering first 60 persons out of 120 persons with

first 10 images per person are considered which leads to 600 images in

the database and the thirteenth image from first 60 persons is considered

as a test image to compute FRR and TSR. The remaining 60 persons out

of 120 are considered as out of database to compute FAR. The samples of

NIR face images are shown in Figure 4. 2.

(ii) L-Spacek

The total number of persons in the L – Spacek are 120. The first 65

persons are considered for database and reaming 55 persons are

considered out of database. Each person has 19 images in that first 10

images per person are considered to create data base which leads to a total

of 650 images and thirteenth image of the first 65 persons taken as test

image to compute the FRR and TSR. The FAR is computed using 55

persons out of data base images. The samples of L-Spacek face images

are shown in Figure 4.3.

78

Fig. 4.2: Samples of NIR Face Images of a Person

79

Fig. 4.3: Samples L- Spacek Face Images of a Person

(iii) JAFFE:

The face database consists of 10 persons with approximatly 20 images

per person. The database is created by considering first 5 persons out of

10 persons and first 10 images per person are considered to create data

base which leads to 50 images in the database and fourteenth image from

80

first 5 persons are taken as test image to compute FRR and TSR. The

remaining 5 persons out of 10 are considered as out of database to

compute FAR. The samples of JAFFE database is shown in Figure 4.4.

Fig. 4.4: Samples of JAFFE Face Images of a Person

81

4.2.2 Preprocessing

The color image is converted into gray scale images. The original size

of Face images are re-sized to the required sizes.

4.2.3 Wavelet Families

The wavelet transform represents a signal in terms of mother wavelets

using dilation and translation. The wavelets are oscillatory functions

having finite duration both in time and in frequency, hence represents in

both spatial and frequency domains. The features extracted by wavelet

transform gives better results in recognition as well as in bifurcating low

frequency and high frequency components as approximation band and

detailed bands respectively.

Advantages of Discrete wavelet transform are; It gives information

about both time and frequency of the signal, Transform of a non-

stationary signal is efficiently obtained, Reduces the size without losing

much of resolution, Reduces redundancy and Reduces computational

time.

Disadvantage of DWT are; Lack of shift invariance, Lack of

directional selectivity for higher dimensionality, unsatisfactory

reconstruction and It has more redundancy compare to DTCWT.

There are a number of basis functions that can be used as the

mother wavelet for Wavelet Transformation. Since the mother wavelet

82

produces all wavelet functions used in the transformation through

translation and scaling, it determines the characteristics of the resulting

Wavelet Transform. Therefore, the details of the particular

application should be taken into account and the appropriate mother

wavelet should be chosen in order to use the Wavelet Transform

effectively.

(a) (b) (c)

Fig. 4.5: Wavelet families (a) Haar (b) Daubechies (c) Symlet2

Figure 4.5 illustrates some of the commonly used wavelet

functions. Haar wavelet is one of the oldest and simplest wavelet.

Therefore, any discussion of wavelets starts with the Haar wavelet.

Daubechies wavelets are the most popular wavelets. They

represent the foundations of wavelet signal processing and are

used in numerous applications. These are also called Maxflat

wavelets as their frequency responses have maximum flatness at

frequencies 0 and π. This is a very desirable property in some

83

applications. The Haar, Daubechies, Symlets and Coiflets are

compactly supported orthogonal wavelets. These wavelets along with

Meyer wavelets are capable of perfect reconstruction.

In DWT, the most prominent information in the signal appears in

high amplitudes and the less prominent information appears in very

low amplitudes. Data compression can be achieved by discarding these

low amplitudes. The wavelet transforms enables high compression ratios

with good quality of reconstruction. At present, the application of

wavelets for image compression is one the hottest areas of

research. Recently, the Wavelet Transforms have been chosen for the

JPEG 2000 compression standard.

4.2.4 Wavelet Transform

Wavelet is an irregular and asymmetric waveform of effectively

limited duration that has an average value of zero. Wavelet Transform

is used to analyse non stationary signals i.e., whose frequency response

varies in time. Wavelet Transform is capable of providing time and

frequency information simultaneously. The wavelet transform is

created by repeatedly filtering the image coefficients on a row-by-row

and column-bycolumn basis. The usefulness of wavelets in image

steganography lies in the fact that the wavelet transform clearly

separates high-frequency and low-frequency information on a pixel-by-

84

pixel basis. The cover image is passed through wavelet filter bank.

Image convolved with wavelet low pass filter gives smooth version of

the input image and that with high pass filter results in the detail band.

This decomposition can be carried up to log2{min(height, width)}. The

low pass coefficients of final level decomposition of the image

constitute approximation band. The low-frequency wavelet coefficients

(approximation band coefficients Ai) are generated by averaging the

two pixel values as given in Equation 4.1 and the high frequency

coefficients (detail band coefficients Di) are generated by taking half of

the difference of the same two pixels as given in equation 4.2.

Ai = …. (4.1)

Di = …. (4.2)

Where Pi is the ith

pixel value in the input spatial domain signal

sequence. A image is decomposed into various wavelet sub-bands,

shown in Figure 4.6, such as Approximation band, Vertical Detail

bands, Horizontal Detail bands, and Diagonal Detail Bands. The

Approximation band consists of the low frequency wavelet coefficients,

which contains significant part of the spatial domain image. A detail

band consists of high frequency coefficients, which contains edge

P2i – 1 + P2i

2

P2i – 1 - P2i

2

85

details of spatial domain image. There are two types of wavelet

transforms viz., Continuous Wavelet Transform (CWT) and Discrete

Wavelet Transform(DWT).

Continuous Wavelet Transform was developed as an alternative

approach to the short term Fourier Transform to overcome the

resolution problem. Where as Discrete Wavelet Transform provides

sufficient information both for analysis and synthesis of the signal with

a significant reduction in the computation time.

Figure 4.7 gives the two Dimensional Discrete Wavelet Transform

decomposing process. The rows of an image are convolved with the

system function of low pass filter and high pass filter to get convoluted

signal. These convoluted signals are down sampled by keeping even

indexed columns. They again are convoluted with transfer functions of

low pass filter and high pass filter. The final convoluted signals are

down sampled to rows, to generate the approximation band and

detailed band.

86

Fig. 4.6: Wavelet Decomposition

Approximation Band

Horizontal Detail Band DWT 2

Vertical Detail Band

Diagonal Detail Band

87

Fig. 4.7: Block Diagram of 2 DWT Decomposition Process

4.2.5 Fast Fourier Transform (FFT)

The FFT is applied on spatial domain image to obtain FFT

coefficients. The features are extracted from FFT [153] coefficients are

real part, imaginary part, magnitude value and phase angle. The FFT

computation is fast compared to Discrete Fourier Transform (DFT)

[154], since the number of multiplications required to compute N-point

DFT are less i.e., only in FFT as against N2 in DFT. The efficiency of

the FFT algorithm can be enhanced for real input signals by forming

complex-valued sequences from the real-valued sequences prior to the

computation of the FFT. The value of FFT can be obtained by using the

Equation 4.3.

88

... (4.3)

Where ;

0 ≤ m ≤ M -1

0 ≤ n ≤ N -1

4.2.6 Features

The features of DWT are obtained from approximation band only.

The features of FFT are computed using the magnitude values.

4.2.7 Matching

The features of test image is compared with features of database

images using Euclidian Distance with the Equation 4.4.

… (4.4)

Where, M = the dimension of feature vector.

Pi = is the database feature vector.

qi = is the test feature vector.

89

4.3 ALGORITHM

Problem Definition

The proposed algorithm is used to analyse the performance of face

recognition using different wavelet families and FFT transformation for

different Face database is given in the Table 4.1.

The objectives are;

• Fingerprint verification to authenticate a person.

• To achieve high TSR

• To have FRR and FAR very low

Table 4.1: Algorithm of PCFTD

Input: Face Database, Test Face Image

Output: Recognition of a person

Step 1: Face image is read from data base.

Step 2: Colored image is converted in to Gray Scale.

Step 3: Image is resized

Step4: Haar, Symlet and DB1 of DWTs and FFT are applied to generate features

Step 5: Repeat step 1 to 4 for test image.

Step 6: Test features are compared with database features using Euclidean distance.

Step 7: Image with Euclidean distance less than threshold value is considered as

matched image otherwise not matching.

90

Original Fingerprint image is of size 480 X 300. An observing the

DB3_A of FVC 2004, we crop the input image to the size 401 X 201 in

order to remove the unwanted portion in the image. And then the cropped

image is resized into 512 X 256 for the DTCWT requirement.

4.4 PERFORMANCE ANALYSIS

The face databases viz., JAFFE, L-Spacek and NIR are considered to

test the algorithm for performance analysis. The frequency domain

transformation FFT and transformation domain DWT with different

wavelets are used to compute FAR, FRR and TSR. The values were

compared to draw the conclusions.

(i) Performance Using FFT

The Table 4.2 gives the variations of FAR, FRR and TSR with respect

to threshold values for different face database with FFT transformation.

FRR decreases whereas FAR increases from 0 value to 100% as threshold

value increases from 0 to 5.

91

Table 4.2: Performance on Different Face Databases with FFT

Threshold

FFT

L – Spacek NIR JAFFE

FAR FRR %

TSR FAR FRR

%

TSR FAR FRR

%

TSR

0 0 1 0 0 1 0 0 1 0

0.25 0 1 0 0 0.85 15.38 0 1 0

0.5 0 0.98 1.54 0 0.63 36.92 0 1 0

0.75 0 0.89 10.77 0 0.49 50.77 0 1 0

1 0 0.80 20 0 0.34 66.15 0 1 0

1.25 0 0.70 29.23 0.02 0.28 72.31 0 1 0

1.5 0 0.55 44.62 0.11 0.25 75.38 0 1 0

1.75 0 0.40 60 0.19 0.22 78.46 1 0.8 20

2 0 0.30 69.23 0.22 0.17 83.08 2 0.6 40

2.25 0 0.16 83.08 0.37 0.15 84.62 2 0.6 40

2.5 0 0.15 84.62 0.44 0.14 86.15 2 0.6 40

2.75 0 0.13 86.15 0.52 0.12 86.15 2 0.6 40

3 0 0.07 92.31 0.69 0.06 90.77 2 0.6 40

3.25 0 0.06 93.85 0.74 0.06 90.77 2 0.6 40

3.5 0 0.04 95.38 0.83 0.03 93.85 2 0.6 40

3.75 0 0.04 95.38 0.91 0.03 93.85 2 0.6 40

4 0 0.04 95.38 0.93 0.03 93.85 3 0.4 60

4.25 0 0.03 96.92 0.93 0.02 93.85 5 0 100

4.5 0 0 100 0.93 0 95.38 5 0 100

4.75 0 0 100 0.94 0 95.38 5 0 100

5 0 0 100 0.94 0 95.38 5 0 100

92

The success rate of recognition is 100% in the case of L-Spacek and

JAFFE face images while success rate is 95% in the case of NIR face

database. Hence FFT is better for L-Spacek and JAFFE face databases.

The variations of FAR and FRR with threshold for L-Spacek, JAFFE and

NIR face databases with FFT are shown in Figure 4.8, 4.9 and 4.10.

Fig. 4.8: FAR and FRR with Threshold for L-Spacek Database

93

Fig. 4.9: FAR and FRR with Threshold for JAFFE Database

Fig. 4.10: FAR and FRR with Threshold for NIR Database

94

(ii) Performance Using Wavelet Families

The performance parameters viz., FAR, FRR and TSR values are

varying with threshold values for different databases such as L- Speack,

NIR and JAFFE with DWT families are given in Tables 4.3, 4.4 and 4.5

respectively. The success rate for L- Speack, and JAFFE database is

100% compared to 95% of success rate for NIR database.

The variations of FAR and FAR with threshold values for L–Spacek

face database using Haar, Symlet and DB1 wavelets are shown in Figure

4.11, 4.12 and 4.13 respectively. The FRR and FAR values are decreasing

and increasing as threshold increases. The value of EER is 0.01 for Haar

and DB1 wavelets compared to EER value 0.2 in the case of Symlet.

Hence Haar and DB1 are better wavelets for L- Spacek face database

compared to Symlet.

95

Table 4.3 Performance Parameters of L- Spacek Databases

Threshold

L – Spacek

Haar Symelet DB1

FAR FRR %

TSR FAR FRR

%

TSR FAR FRR

%

TSR

0 0 1 0 0 1 0 0 1 0

0.25 0 0.98 1.54 0 1 0 0 0.98 1.54

0.5 0 0.88 12.31 0 1 0 0 0.88 12.31

0.75 0 0.71 29.23 0 1 0 0 0.71 29.23

1 0 0.51 49.23 0 1 0 0 0.51 49.23

1.25 0 0.31 69.23 0 0.8 20 0 0.31 69.23

1.5 0 0.25 75.38 0 0.6 40 0 0.25 75.38

1.75 0 0.18 81.54 0 0.6 40 0 0.18 81.54

2 0 0.09 90.77 0 0.6 40 0 0.09 90.77

2.25 0 0.06 93.85 0 0.6 40 0 0.06 93.85

2.5 0 0.05 95.38 0 0.6 40 0 0.05 95.38

2.75 0 0.05 95.38 0 0.6 40 0 0.05 95.38

3 0 0.05 95.38 0.25 0.2 80 0 0.05 95.38

3.25 0 0.03 96.92 0.25 0.2 80 0 0.03 96.92

3.5 0.02 0.03 96.92 0.25 0.2 80 0.02 0.03 96.92

3.75 0.02 0 100 0.25 0.2 80 0.02 0 100

4 0.06 0 100 0.25 0 100 0.06 0 100

4.25 0.15 0 100 0.25 0 100 0.15 0 100

4.5 0.17 0 100 0.25 0 100 0.17 0 100

4.75 0.24 0 100 0.25 0 100 0.24 0 100

5 0.31 0 100 0.25 0 100 0.31 0 100

96

Table 4.4: Performance Parameters of JAFFE Databases

Threshold

JAFFE

Haar Symlet DB1

FAR FRR %

TSR FAR FRR

%

TSR FAR FRR

%

TSR

0 0 1 0 0 1 0 0 1 0

0.25 0 1 0 0 1 0 0 1 0

0.5 0 1 0 0 1 0 0 1 0

0.75 0 1 0 0 1 0 0 1 0

1 0 1 0 0 1 0 0 1 0

1.25 0 1 0 0 0.8 20 0 0.8 20

1.5 0 0.8 20 0 0.6 40 0 0.6 40

1.75 0 0.8 20 0 0.6 40 0 0.6 40

2 0 0.6 40 0 0.6 40 0 0.6 40

2.25 0 0.6 40 0 0.6 40 0 0.6 40

2.5 0 0.6 40 0 0.6 40 0 0.6 40

2.75 0 0.6 40 0 0.6 40 0 0.4 60

3 0 0.6 40 0.25 0.2 80 0.25 0.2 80

3.25 0 0.6 40 0.25 0.2 80 0.25 0.2 80

3.5 0 0.4 60 0.25 0.2 80 0.25 0.2 80

3.75 0.25 0.2 80 0.25 0.2 80 0.25 0.2 80

4 0.25 0.2 80 0.25 0 100 0.25 0 100

4.25 0.25 0.2 80 0.25 0 100 0.25 0 100

4.5 0.25 0.2 80 0.25 0 100 0.25 0 100

4.75 0.25 0 100 0.25 0 100 0.25 0 100

5 0.25 0 100 0.25 0 100 0.25 0 100

97

Table 4.5: Performance Parameters of NIR Databases

Threshold

NIR

Haar Symelet DB1

FAR FRR %

TSR FAR FRR

%

TSR FAR FRR

%

TSR

0 0 1 0 0 1 0 0 1 0

0.25 0 0.69 30.77 0 0.72 27.69 0 0.69 30.77

0.5 0 0.45 55.38 0 0.51 49.23 0 0.45 55.38

0.75 0 0.28 72.31 0 0.29 70.77 0 0.28 72.31

1 0.15 0.22 78.46 0.13 0.22 78.46 0.15 0.22 78.46

1.25 0.35 0.17 83.08 0.33 0.17 83.08 0.35 0.17 83.08

1.5 0.52 0.08 90.77 0.52 0.08 90.77 0.52 0.08 90.77

1.75 0.8 0.03 93.85 0.78 0.03 93.85 0.8 0.03 93.85

2 0.93 0.02 93.85 0.93 0.02 93.85 0.93 0.02 93.85

2.25 0.93 0.02 93.85 0.93 0.02 93.85 0.93 0.02 93.85

2.5 0.94 0 93.85 0.94 0 95.38 0.94 0 93.85

2.75 0.96 0 93.85 0.94 0 95.38 0.96 0 93.85

3 0.96 0 93.85 0.96 0 95.38 0.96 0 93.85

3.25 0.98 0 93.85 0.98 0 95.38 0.98 0 93.85

3.5 0.98 0 93.85 0.98 0 95.38 0.98 0 93.85

3.75 0.98 0 93.85 0.98 0 95.38 0.98 0 93.85

4 0.98 0 93.85 0.98 0 95.38 0.98 0 93.85

4.25 0.98 0 93.85 0.98 0 95.38 0.98 0 93.85

4.5 0.98 0 93.85 0.98 0 95.38 0.98 0 93.85

4.75 0.98 0 93.85 0.98 0 95.38 0.98 0 93.85

5 0.98 0 93.85 0.98 0 95.38 0.98 0 93.85

98

Fig. 4.11: FAR and FRR with Threshold for L–Spacek with DWT

Fig. 4. 12: FAR and FRR with Threshold for L–Spacek with DWT

Haar Wavelet

Symlet Wavelet

99

Fig. 4.13: FAR and FRR with Threshold for L–Spacek with DWT

The variations of FAR and FAR with threshold values for JAFFE



and increasing as threshold increases. The value of EER is 0.2 for Haar,

Symlet and DB1 wavelets. Hence Haar, Symelt and DB1 are equal

wavelets for JAFFE face database.

DB1 Wavelet

100

Fig. 4.14 FAR and FRR with Threshold for JAFFE Databases with DWT

Fig. 4.15: FAR and FRR with Threshold for JAFFE with DWT

Haar Wavelet

Symlet Wavelet

101

Fig. 4.16: FAR and FRR with Threshold for JAFFE with DWT

Fig. 4.17: FAR and FRR with threshold for NIR databases with DWT

DB1 Wavelet

Haar Wavelet

102



Symlet Wavelet

DB1

103

The variations of FAR and FAR with threshold values for JAFFE



and increasing as threshold increases. The value of EER is 0.2 for Haar,

Symlet and DB1 wavelets. Hence Haar, Symelt and DB1 are equal

wavelets for NIR face database.

EER values with different transformation and face image database

are tabulated in the Table 4.6. It is observed that the EER values are better

in the case of FFT compared to DWTs. The performance with L- Speack

database is better compared to JAFFE and NIR with both DWT and FFT

transformations.

Table 4.6: EER Values For Different Transforms

Database

EER

DWT FFT

Haar Symlet DB1

L – Speack 0.01 0.2 0.01 0

JAFFE 0.2 0.2 0.2 0.15

NIR 0.2 0.2 0.2 0.2

104

4.5 SUMMARY

Face recognition is a physiological biometric trait. The different face

data bases are considered for performance analysis. The PCFTD model of

Face Recognition using Haar, Symlet and Dd1 of DWTs and FFT is

proposed. The features of face images are obtained using Haar, Symlet

and DB1 wavelets as well as FFT transforms. The features of test image

are compared with database images using Euclidian Distance (ED). The

performance parameters such as FAR, FRR and TSR are computed using

different transform on different face databases. It is observed that the

performance of FFT is better compared to DWT.

CHAPTER 5

CONCLUSIONS

105

CHAPTER 5

CONCLUSIONS

5.1 INTRODUCTION

Biometrics identifies people by measuring some aspect of individual

anatomy or physiology such as hand geometry or fingerprint, some deeply

ingrained skill or other behavioral characteristic such as handwritten

signature or something that is a combination of the two such as voice. By

the implementation of biometrics technology, the fear of stolen, lost or

forget of the traditional ways of authentication can be eliminated.

Biometrics refers to the automatic identification (or verification) of an

individual (or a claimed identity) by using certain physiological or

behavioral traits associated with the person.

The proposed Transform Domain Fingerprint Identification based on

DTCWT is presented in chapter 3. The fingerprint is cropped and resized

to dimensions of 2m X 2

n which is suitable for DTCWT. The features of

fingerprint are extracted by applying seven levels of DTCWT. The

features are generated by concatenating magnitude and phase of DTCWT.

The test image features are compared with database images using

Euclidian Distance (ED). The level of DTCWT gives better results in

terms of EER and TSR.

106

In chapter 4, the Performance Comparison of Face Recognition using

Transform Domain technique is proposed. The face databases such as L-

Spacek, JAFFE and NIR are considered for the performance analysis. The

face images are resized to a required dimensions and color images are

converted into gray scale images. The wavelet families viz., Haar, Symelt

and DB1 are applied on face images to derive four subbands such as LL,

LH, HL and HH. The face image features are obtained using LL subband

only. The FFT is also applied on face images to extract features using

magnitudes. The test image features are compared with database images

usinig ED. The perforamce parameters such as FRR, FAR, TSR and EER

are evaluated using wavelet families and FFT on L- Spacek, JAFFE and

NIR face databases. The performance of the algorithm is compared using

wavelet families and FFT. The EER values are better in the case of FFT

compared to wavelet families on different face databases.

5.2 CONTRIBUTIONS OF THIS WORK

The different levels of DTCWT are applied on cropped and resized

fingerprint images. The features of fingerprint are extracted by

concatenating magnitude and phase of DTCWT. The test image features

are compared with database images using ED. The performance results

are compared using seven levels of DTCWT. The proposed algorithm

gives better results for level seven of DTCWT.

107

The different wavelet families and FFT are used to verify the face

recognition algorithm. The face databases of L- Spacek, JAFFE and NIR

are used to test the algorithm. The face image are resized and the wavelet

families such as Haar, Symlet and DB2 are applied to derive low and

high frequency components. The features of face images are extracted by

considering low frequency components only.

The FFT is also applied on face images to extract features using

magnitudes. The features of test image is compared with database images

using ED. The performance of an algorithm is compared using wavelet

families and FFT. The proposes algorithm gives better result with FFT

compared with the wavelet families.

5.3 FUTURE WORK

Fingerprint verification can be tested with the combination of Spatial

and Transform Domain techniques.

The fingerprint can be segmented into small parts and apply Spatial or

Transform Domain techniques on segments simultaneous to generate

features for better results.

Face identification can be test with Hybrid Domain techniques

The fingerprint and face features are combined to identify a person

effectively.

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108

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Image Processing, vol. 1, pp. 227–230, 2004.

[151] M Miller, N Kingsbury, and R Hobbs, “Seismic Imaging using

Complex Wavelets,” Proceeding of IEEE International Conference

on Acoustic Speech and Signal Processing, vol. 2, pp. 557–560,

2005.

[152] E Causevic, R John, J Kovacevic, and A. Jacquin “Adaptive

Complex Wavelet based Filtering of EEG for Extraction of Evoked

Potential Responses,” Proceeding of IEEE International

Conference on Acoustic Speech and Signal Processing, vol. 5, pp.

393–396, March 2005.

[153 ] Saidi A, “Decimation-in-time-frequency FFT algorithm,” IEEE

International Conference on Acoustics, Speech, and Signal

Processing, vol. 3, pp. 453 – 456, 1994.

[154 ] Papouli E V, Stathald T, “A DFT algorithm based on filter banks:

the extended subband DFT,” Proceedings of International

Conference on Image Processing, vol. 1, pp. 1- 6, 2003 .

129

LIST OF PUBLICATIONS BASED ON THE THESIS

International Journals

[1] Jossy P George, S K Abhilash and K B Raja, “Transform Domain

Fingerprint Identification Based on DTCWT,” International Journal

of Advanced Computer Science and Applications, vol. 3, no.1, pp. 190-

198, 2012.

[2] Jossy P George, Saleem S Tevarmani and K B Raja, “Performance

Comparison of Face Recognition using Transforms Domain

Techniques,” World of Computer Science and Information Technology

Journal, vol. 2, no.3, pp. 82-86, 2012.

International Conferences

[3] Jossy P George, S K Abhilash, M D Chethana and K B Raja,

“Performance Analysis of Fingerprint Identification Using Different

Levels of DTCWT,” Proceedings of International Conference on

Information and Computer Applications, Hong Kong, vol. 24, pp. 185-

192, February, 2012.

[4] Jossy P George and K B Raja, “Performance Analysis of Face

Recognition using Wavelet Families and FFT,” International

Conference on Computer Technology and Science, New Delhi, 2012

(Communicated).

130

Appendix A Publications

131

Appendix B MATLAB TUTORIAL

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APPENDIX B

MATLAB TUTORIAL

Introduction

MATLAB is an abbreviation for "Matrix Laboratory." It is an

interactive and high performance language for numerical computations

and graphics which is faster than with traditional programming languages

such as C, C++, and Fortran. MATLAB is mainly used for the matrix

computations. All MATLAB variables are multidimensional arrays, no

matter what type of Data. All the problems and solutions are expressed in

the mathematical notations. The main uses of MATLAB includes; Math

and computation, Algorithm development, Data acquisition, Modelling,

simulation and prototyping, Data analysis, exploration and visualization,

Scientific and engineering graphics, Application development etc. Since,

MATLAB is designed to solve problems numerically, that is, infinite

precision arithmetic. So it produces approximate rather than exact

solutions.

1. Starting/quitting

To start using the MatLab, click on the ‘Start’ button on the left

bottom of the screen, and then click on ‘All Programs’, then ‘Math and

Stats’, then ‘Matlab’. A window will pop up that will consist of three

smaller windows. On the right there will be a big window entitled

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‘Command Window’. On the left there will be two windows, one entitled

‘Workspace’ and another one ‘Command History’. In the Command

window type quit (the letters should appear after the prompt) and hit

enter. Matlab will close.

2. A = imread (filename, fmt)

This command helps to read a gray scale or color image from the

file specified by the string filename. If the file is not in the current

directory, or in a directory on the MATLAB path, specify the full path

name.

The text string fmt specifies the format of the file by its standard file

extension. For example, specify ‘gif’ for Graphics Interchange Format

files. To see a list of supported formats, with their file extensions, use the

imformats function. If imread cannot find a file named filename, it looks

for a file named filename.fmt.

The return value A is an array containing the image data. If the file

contains a grayscale image, A is an M-by-N array. Fi the file contains a

true color image, A is an M-by-N-by-3 array. For TIFF files containing

color images that use the CMYK color space, A is an M-by-N-by-4 array.

See TIFF in the Format-Specified information section for more

information.

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The class of A depends on the bits-per-sample of the image data,

rounded to the next byte boundary. For example, imread returns 24 bit

color data as an array of unit data because the sample size for each color

component is 8 bits. See remarks for a discussion of bitdpeths, and see

Format-Specific Information for more detail about supported bitdepths

and sample sizes for a particular format.

[X, map] = imread(…) reads the indexed image in filename into X and its

associated colormap into map. Colormap values in the image file are

automatically rescaled into the range [0, 1].

[…] = imread (filename) attempts to infer the format of the file from its

content.

3. Imshow(I)

Imshow(I) displays the grayscale image I. Imshow is an image

processing toolbox command and this deals the matrix as an image. It

assumes that the elements are pixel intensities.

imshow(I,[low high]) displays the grayscale image I, specifying the

display range for I in [low high]. The value low (and any value less than

low) displays as black; the value high (and any value greater than high)

displays as white. Values in between are displayed as intermediate shades

of gray, using the default number of gray levels. If you use an empty

matrix ([]) for [low high], imshow uses [min(I(:)) max(I(:))]; that is, the

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minimum value in I is displayed as black, and the maximum value is

displayed as white.

imshow(RGB) displays the true color image RGB.

imshow(BW) displays the binary image BW. imshow displays pixels with

the value 0 (zero) as black and pixels with the value 1 as white.

imshow(X,map) displays the indexed image X with the colormap map. A

color map matrix may have any number of rows, but it must have exactly

3 columns. Each row is interpreted as a color, with the first element

specifying the intensity of red light, the second green, and the third blue.

Color intensity can be specified on the interval 0.0 to 1.0.

4. edge

BW = edge(I) takes a grayscale or a binary image I as its input, and

returns a binary image BW of the same size as I, with 1's where the

function finds edges in I and 0's elsewhere.

By default, edge uses the Sobel method to detect edges but the following

provides a complete list of all the edge-finding methods supported by this

function:

The Sobel method finds edges using the Sobel approximation to the

derivative. It returns edges at those points where the gradient of I is

maximum.

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The Prewitt method finds edges using the Prewitt approximation

to the derivative. It returns edges at those points where the gradient of I is

maximum.

The Roberts method finds edges using the Roberts approximation

to the derivative. It returns edges at those points where the gradient of I is

maximum.

The Laplacian of Gaussian method finds edges by looking for zero

crossings after filtering I with a Laplacian of Gaussian filter.

The zero-cross method finds edges by looking for zero crossings

after filtering I with a filter you specify.

The Canny method finds edges by looking for local maxima of the

gradient of I. The gradient is calculated using the derivative of a Gaussian

filter. The method uses two thresholds, to detect strong and weak edges,

and includes the weak edges in the output only if they are connected to

strong edges. This method is therefore less likely than the others to be

fooled by noise, and more likely to detect true weak edges.

5. fft2 / ifft2

The MATLAB functions fft, fft2, and fftn (and their inverses ifft,

ifft2, and ifftn, respectively) all use fast Fourier transform algorithms to

compute the DFT.

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Y = fft2(X) returns the two-dimensional discrete Fourier transform (DFT)

of X, computed with a fast Fourier transform (FFT) algorithm. The result

Y is the same size as X.

Y = fft2(X,m,n) truncates X, or pads X with zeros to create an m-by-n

array before doing the transform. The result is m-by-n. Ifft2 MATLAB

function returns the two-dimensional inverse discrete Fourier transform

(DFT) of X, computed with a fast Fourier transform (FFT) algorithm

Y = ifft2(X) returns the two-dimensional inverse discrete Fourier

transform (DFT) of X, computed with a fast Fourier transform (FFT)

algorithm. The result Y is the same size as X.

ifft2 tests X to see whether it is conjugate symmetric. If so, the

computation is faster and the output is real. An M-by-N matrix X is

conjugate symmetric if X(i,j) =conj(X(mod(M-i+1, M) + 1, mod(N-j+1,

N) + 1)) for each element of X. Y = ifft2(X,m,n) returns the m-by-n

inverse fast Fourier transform of matrix X.

y = ifft2(..., 'symmetric') causes ifft2 to treat X as conjugate symmetric.

This option is useful when X is not exactly conjugate symmetric, merely

because of round-off error. y = ifft2(..., 'nonsymmetric') is the same as

calling ifft2(...) without the argument 'nonsymmetric'.

For any X, ifft2(fft2(X)) equals X to within roundoff error.

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6. Concatenation

Concatenation is the process of joining arrays to make larger ones.

In fact, you made your first array by concatenating its individual

elements. The pair of square brackets[] is the concatenation operator.

A = [a,a] A=

1 2 3 1 2 3

4 5 6 4 5 6

7 8 10 7 8 10

Concatenating arrays next to one another using commas is called

horizontal concatenation. Each array must have the same number of rows.

Similarly, when the arrays have the same number of columns, you can

concatenate vertically using semicolons.

A = [a; a] A=

1 2 3

4 5 6

7 8 10

1 2 3

4 5 6

7 8 10

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