FINGERPRINT VERIFICATION BASED ON …ir.fsksm.utm.my/3612/1/MohammedPC073030d10ttp.pdf · BAHAGIAN...

FINGERPRINT VERIFICATION BASED ON STATISTICAL FEATURES

OF CO-OCCURRENCE MATRICES

MOHAMMED MOHAMMED SAYIM KHALIL

UNIVERSITI TEKNOLOGI MALAYSIA

PSZ l9:16 (Pind. l /07)

UNIVERSITI TEKNOLOGI MALAYSIA

NOTES : * tf the thesis is CONFIDENTAL or RESTRICTED, pleose ottoch with the letfer fromfhe orgonizotion with period ond reosons for confidentiolity or resfriction.

DECTARATION OF THESIS / UNDERGRADUATE PROJECT PAPER AND COPYRIGHT

Author 's ful l nomeDote of birth

Title

Mohommed Mohommed Soyim Khol i lJonuory 24,1958

Fingerprint Verificolion Bosed on Stotisticol Feotures ofCo-occurrence Mof rices

Acodemic Session: 2OO9 /2010 - ll

I declore thot this thesis is clossified os :

EnE

CONFIDENTIAI (Contoins confidentiol informotion under the Off iciol SecrelAcI 1972)"

RESTRICTED (Contoins restricted informotion os specified by fheorgonizotion where reseorch wos done)*

I ocknowOPEN ACCESS logree thol my lhesis to be published os online open occess

(ful l fext)

l. The thesis is the properfy of Universili Teknologi Moloysio.2. The Librory of UniversitiTeknologi Moloysio hos the right fo moke copies for the purpose' of reseorch onlY.3. The Librory hos lhe right fo moke copies of lhe lhesis for ocodemic exchonge'

SIGNAIURE OF SUPERVISOR

Prof. Dr. Dzulkifli bin MuhammadNAME OF SUPERVISOR

Dofe : 74.- >- ?p (0 Dote : |y'{ -3 -U/ r

"I hereby declare that I have read this thesis and in my

opinion this thesis is sufficient in terms of scope and quality for the

award of the degree of Doctor of Philosophy (computer Science)."

Signature

Name of SuPervisor :

Date :

BAHAGIAN A – Pengesahan Kerjasama*

Adalah disahkan bahawa projek penyelidikan tesis ini telah dilaksanakan melalui

kerjasama antara _______________________ dengan _______________________

Disahkan oleh:

Tandatangan : Tarikh :

Nama :

Jawatan :

(Cop rasmi)

* Jika penyediaan tesis/projek melibatkan kerjasama.

BAHAGIAN B – Untuk Kegunaan Pejabat Sekolah Pengajian Siswazah

Tesis ini telah diperiksa dan diakui oleh:

Nama dan Alamat Pemeriksa Luar :

Prof. Madya Dr. Abd Rahman bin Ramli Department of Computer & Communication Systems Engineering, Faculity of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor

Nama dan Alamat Pemeriksa Dalam :

Prof. Dr. Ghazali bin Sulong

Faculti Sains Komputer dan Sistem Maklumat,

UTM, Skudai.

Nama Penyelia lain (jika ada) :

-

Disahkan oleh Timbalan Pendaftar di Sekolah Pengajian Siswazah:

Tandatangan : Tarikh :

Nama : ZAINUL RASHID BIN ABU BAKAR

FINGERPRINT VERIFICATION BASED ON STATISTICAL FEATURES OF

CO-OCCURRENCE MATRICES

MOHAMMED MOHAMMED SAYIM KHALIL

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Doctor of Philosophy (Computer Science)

Faculty of Computer Science and Information System

Universiti Teknologi Malaysia

MARCH 2010

I declare that this thesis entitled "Fingerprint Yerification Based on Statistical

Features af Co-occurrence Matrices" is the result of my own research except as cited

in the references. The thesis has not bee,n acce,pted for any degree and is not

concurrently subrnitted in candidature of any other degree.

Signature :

Name : Mohammed Mohammed Sayim Khalil

Dato :Lq^1. -?@(a

iii

To my God, Allah 'azza wa jalla

Then to my beloved mother, wife, children and parents-in law.

iv

ACKNOWLEDGEMENT

Thanks to ALLAH for everything I was able to achieve and for everything I tried

but I was not able to achieve.

I am grateful for several people for making this thesis possible. I would like

to begin by expressing my deep appreciation to my advisor, Professor Dr. Dzulkifli

bin Muhammad for his professional and personal advice, guidance and help. Despite

the busy schedule, he has provided me with valuable ideas, encouragement and

comments. I am very fortunate to have him as my adviser. I would like to thank

Osama Mohmmud El-Aryani for his personal support. I also would like to thank my

examiners Professor Dr. Ghazali bin Sulong for his comments and suggestions and

Professor Madya Dr. Abd Rahman bin Ramli.

I would like to thank my wonderful wife for understanding and love. You are

always there when I needed support. You are the only reason I worked hard.

v

ABSTRACT

Fingerprint is one of the first and most popular forms of biometric to be used

for personal identification. Matching fingerprint images is an extremely difficult

problem, mainly due to the large variability in different impressions of the same

finger, also refereed as intra-class variations. The main factors responsible for intra-

class variations are displacement, orientation, partial overlap, non-linear distortion,

and various noises. At present, fingerprint verification entails three distinct classes of

techniques: minutiae based, correlation based and hybrid based. However, these

methods suffer mainly from feature selection and extraction error as a result of

inaccurate detection of minutiae due to missed alignment, either by minutiae or

reference point. In this thesis, new techniques are proposed for fingerprint

verification based on statistical analysis of co-occurrence matrices to overcome the

drawbacks of previous methods. The proposed method principally involves five

steps: enhancing the fingerprint image using the short time Fourier Transform

analysis; detecting singular point based on fingerprint orientation field reliability;

extracting and normalizing the region of interest; selecting and extracting the

fingerprint feature; and fingerprint verification. Experiments were conducted using

the Second International Competition for Fingerprint Verification Algorithms

databases (FVC2002). Results were tested using two standard protocols; FVC2002

and the Program for Rate Estimation and Statistical Summaries (PRESS). The equal

error rate for experiments 1, 2, 3 and 4 produced by FVC2002 protocol are 0.43%,

0.38%, 0.32% and 0.26% and those produced by PRESS are 0.45%, 0.37%, 0.34%

and 0.29% respectively. The results show that the proposed method has successfully

minimized the equal error rate in both testing protocols, i.e. less than 0.5%. In the

previous methods, the equal error rate is more than 0.5%. As the equal error rate is an

important factor for evaluating the fingerprint verification’s accuracy, the results

show that the proposed method has successfully contributed toward efficient

fingerprint verification.

vi

ABSTRAK

Sidik jari merupakan di antara bentuk biometrik paling popular dan terawal

yang digunakan untuk pengenalpastian peribadi. Pemadanan imej-imej sidik jari

adalah masalah yang sangat rumit, terutamanya disebabkan oleh perbezaan ketara

terhadap ekspresi-ekspresi berlainan bagi jari yang sama, juga dipanggil sebagai

variasi dalam-kelas atau intra-kelas. Faktor-faktor utama yang bertanggungjawab ke

atas variasi intra-kelas disenaraikan seperti berikut: pengalihan, orientasi,

pertindanan separa, pengherotan tak-linear dan pelbagai kebisingan. Ketika ini,

penentusahan sidik jari merangkumi tiga teknik berlainan; berasaskan perincian,

berasaskan korelasi dan berasaskan hibrid. Namun, kaedah-kaedah ini mempunyai

kelemahan nyata dari aspek pemilihan ciri dan ralat pengekstrakan yang berpunca

daripada ketidaktepatan pengesanan perincian akibat penjajaran tersasar, sama ada

secara perincian atau secara titik rujukan. Dalam tesis ini, teknik-teknik baru

berasaskan analisis statistik matriks kejadian-bersama dicadangkan bagi menangani

kelemahan kaedah-kaedah penentusahan sidik jari terdahulu. Kaedah cadangan

secara prinsipnya melibatkan lima langkah: meningkatkan imej sidik jari

menggunakan analisis masa singkat transformasi Fourier; mengesan titik tunggal

berdasarkan kawasan kebolehpercayaan orientasi sidik jari; mengasingkan dan

menormalkan lingkungan berkepentingan; memilih dan mengekstrak ciri-ciri sidik

jari; dan menentusahkan sidik jari. Ujikaji dijalankan menggunakan pangkalan data

Pertandingan Antarabangsa Kedua untuk Algoritma Penentusahan Sidik Jari

(FVC2002). Keputusan ujikaji diuji berpandukan dua protokol piawai; FVC2002 dan

Program untuk Kadar Penganggaran dan Rumusan Berstatistik (PRESS). Kadar ralat

sama rata untuk eksperimen 1, 2, 3 dan 4 yang dihasilkan oleh protokol FVC2002

ialah 0.43%, 0.38%, 0.32% dan 0.26%, manakala yang dihasilkan oleh PRESS ialah

0.45%, 0.37%, 0.34% dan 0.29%. Perangkaan ini menunjukkan bahawa kaedah

cadangan telah berjaya meminimumkan kadar ralat sama rata bagi kedua-dua

protokol pengujian, iaitu kurang daripada 0.5%. Bagi kaedah-kaedah terdahulu,

kadar ralat sama rata adalah lebih daripada 0.5%. Memandangkan kadar ralat sama

rata merupakan faktor penting untuk menilai ketepatan penentusahan sidik jari,

keputusan ini menunjukkan bahawa kaedah cadangan telah berjaya menyumbang ke

arah matlamat penentusahan sidik jari yang lebih berkesan.

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xi

LIST OF ABBREVIATION xvii

LIST OF SYMBOLS xviii

LIST OF APPENDICES xx

1 INTRODUCTION 1

1.1 Introduction 1

1.2 Background of Research 4

1.3 Problem Statement 6

1.4 Objectives of Study 6

1.5 Scope of Study 7

1.6 Thesis Contributions 8

1.7 Thesis Outline 9

2 LITERATURE REVIEW 10

2.1 Introduction 10

2.2 Fingerprint Orientation Field 10

viii

2.2.1 Gradient-Based 13

2.2.2 Pixel-Mask-Based 13

2.2.3 Pixel-Mask-Based 14

2.3 Singular Points 15

2.3.1 Poincaré Index 17

2.3.2 Local Characteristics of the Orientation Image 28

2.3.3 Partitioning-Based Method 35

2.4 Fingerprint Verification 37

2.4.1 Minutiae-Based 38

2.4.2 Correlation-Based 43

2.4.3 Texture-Based or Image-Based (Hybrid) 43

2.5 Summary 50

3 METHODOLOGY 60

3.1 Introduction 60

3.2 Fingerprint Enhancement 63

3.3 Singular Point Detection 67

3.3.1 Orientation Field 69

3.3.2 Reliability 71

3.3.3 Singular Point Location 74

3.4 Region of Interest 79

3.4.1 Region of Interest Extraction 79

3.4.2 Region of Interest Normalization 80

3.5 Feature Selection and Extraction 82

3.5.1 Feature Selection 83

3.5.2 Feature Extraction 87

3.6 Fingerprint Verification 89

3.7 Summary 90

4 EXPERIMENTAL RESULTS AND DISCUSSIONS 91

4.1 Introduction 91

4.2 Dataset 91

4.3 Singular Point Experiment 98

4.4 Fingerprint Verification Experiment 104

ix

5 CONCLUSION AND FUTURE WORK 120

5.1 Overview 120

5.2 Results Summary 120

5.3 Contributions 122

5.4 Future Work 123

REFERENCES 124

Appendices A - G 130-163

x

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 The 9 x 9 pixel-mask where each number

represents an angle 14

2.2 Core and delta mask 33

2.3 Summary of the fingerprint orientation field

literature review 51

2.4 Summary of the singular point detection

literature review 51

2.5 Summary of the fingerprint verification literature review 57

4.1 Image size and resolution of the Databases 93

4.2 Percentage (%) of false and miss detection of

singular point 98

4.3 Comparison of other and the proposed methods 104

4.4 FAR(%) and FRR(%) for experiment one 106

4.5 EER(%) computed by the protocol and PRESS for

experiment one 107

4.6 FAR(%) and FRR(%) for experiment two 109


experiment two 109

4.8 FAR(%) and FRR(%) for experiment three 112


experiment three 112

4.10 FAR(%) and FRR(%) for experiment four 115


experiment four 115

xi

4.12 Summary EER(%) computed by protocol and PRESS 118

4.12 EER(%) comparison of the proposed method 119

xii

LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1 Ridges and valleys on a fingerprint image 2

1.3 Block diagram of a fingerprint authentication

system 3

2.1 Sample of five classes fingerprints 11

2.2 Fingerprint image with the orientation field 12

2.3 The five types of 2 x 2 pixel neighborhood 15

2.4 Core and Delta position in fingerprint image 16

2.5 Illustration of the directional field around the

singular point 18

2.6 Crossing number property (“1” black in pixel) 39

2.7 Alignment of the input ridge and the template

ridge 40

2.8 A minutia local structure of 2-nearst

neighborhood 41

2.9 Circle region around the reference point 45

2.10 Fingerprint square tessellation image 47

3.1 General methodology block diagram 62

3.2 Low quality fingerprint images 65

3.3 Enhanced fingerprint images 66

3.4 Singular point block method diagram 68

3.5 Sobel mask 69

3.6 Orientation field on original fingerprint 71

xiii

3.7 Orientation field reliability with the singular

point inside the contour 72

3.8 Reliability image map and the peak of the

singular points a) one singular point, b)

two singular points c) three singular points 73

3.9 Region of interest after the segmentation 75

3.10 Singular point contour after thinning 76

3.11 Singular point contour 76

3.12 Filled singular point contour 77

3.13 Singular point pixel 78

3.14 Singular point location on the original fingerprint 78

3.15 Region of interest 80

3.16 Normalized region of interest 82

3.17 Direction and position of the pixel of interest 85

3.18 Co-occurrence matrix generated with direction 0 85



3.21 Co-occurrence matrix generated with

direction 135 87

4.1 Fingerprint images from DB1 94




4.5 A singular point 99

4.6 Two singular points 100

4.7 Three singular points 101

4.8 False singular point 102

4.9 Miss detecting of singular point 103

4.10 ROC graph for DB1 experiment one 107




4.14 ROC graph for experiment one 109

xiv

4.15 ROC graph for DB1 experiment two 110




4.19 ROC graph for experiment two 112

4.20 ROC graph for DB1 experiment three 113




4.24 ROC graph for experiment three 115

4.25 ROC graph for DB1 experiment four 116




4.29 ROC graph for experiment four 118

xvii

LIST OF ABBREVIATIONS

FVC2002 - Second International Competition for Fingerprint

Verification Algorithms

PRESS - Program for Rate Estimation and Statistical Summaries

AFIS - Automatic Fingerprint Identification Systems

FAR - false acceptance rate

FRR - false rejection rate

1-M - one-to-many

SP - singular point

PCA - principal component analysis

MASDF - modified averaged square directional field

2D - two dimensional

WLS - weighted least squares

STFT - Short time Fourier Transform analysis

GHMs - Gaussian–Hermite moments

CM - covariance matrix

DPAG - directed positional four acyclic graphs

TSVM - Transductive Support Vector Machine

AAD - average absolute deviation

WFMT - Wavelet Fourier–Mellin Transform

PWC - Parzen Window Classifier

ROI - region of interest

EWC - Eigenvalue-weighted cosine

FFT - Fast Fourier transform

IFFT - inverse Fast Fourier transform

GLCM - Gray-level co-occurrence matrix

xviii

LIST OF SYMBOLS

- Orientation, Direction

t - Threshold

||X|| - Magnitude (or modulus) of the vector x

x y - Scalar product between vectors x and y

Gx - x-gradient

Gy - y-gradient

Nψ - Poincare index curve points

A - Area

∂A - Contour around the area

[𝐺𝑥𝐺𝑦 ]𝑇 - Gradient vector

D - Orientation field

pd - Average pixel intensity

CluD - Block clusters degree

Imgmean - intensity mean of the whole image

MeanI - difference of local block mean and global image mean

Var - variance of the ridge valley structures

K - Curvature

Gl - two-dimensional low-pass filter

f - derivative of the original image

- Averaged square directional field

Hw - 2D Wiener low-pass filter

Mk - feature vector of a minutia

- flex coefficient

mc - minutia orientation

xix

Vθ - Gabor filtered image

ℱ−1 - Inverse Fourier transform

E(x,y) - Energy Image

O(x,y) - Ridge Orientation Image

O’(x,y) - Smooth orientation image

F(x,y) - Ridge frequency image

C(x,y) - coherence image

Gxx - squared x-gradient

Gyy - squared y-gradient

- Continuous vector

- Reliability

H - Morphological hole filling

I - Image

# - Number of elements

m - Mean

- Standard deviation

xx

LIST OF APPENDICES

APPENDIX TITLE PAGE

A Results from singular point detection type one singular

point 130

B Results from singular point detection type two singular

points 135

C Results from singular point detection type three singular

points 140

D Results from fingerprint verification experiment one 145

E Results from fingerprint verification experiment two 151

F Results from fingerprint verification experiment three 157

G Results from fingerprint verification experiment four 163

CHAPTER 1

INTRODUCTION

1.1 Introduction

Fingerprint probably is the most widely used as a personal identification tool.

It has been used for many centuries, primarily because of its individuality and

uniqueness and reliability.

The fingerprint is a duplicate of a fingertip epidermis when person touches a

smooth surface. The fingertip epidermis characteristic is then transferred to the

surface. The visual characteristics of a fingerprint are a pattern of ridges and valleys

as shown in Figure ‎1.1. They flow in a local constant direction; sometimes they are

terminated and/or bifurcated. The ridges vary in width and having dark color in the

fingerprint image, whereas valleys are bright. When there are cuts or superficial

burns, abrasions happened to the fingertip, the original pattern of the underlying

ridge structure is duplicated in any new skin that grows (Maltoni et al., 2003).

2

Figure ‎1.1 Ridges and valleys on a fingerprint image

There are two types of features used for fingerprint recognition: Global

feature and local feature. Global feature shape is a special pattern of ridge and

valleys, namely as singularities or singular point and may be classified into three

typologies. Loop, delta, and whorl; and the important points are the core and the

delta. A core is defined as the most point on the inner most ridges; and a delta is

defined as the center point where three different direction flows meet, which are used

for fingerprint classification (Bazen and Gerez, 2002). Local feature which is

extracted from fingerprint's ridges information is known as minutiae. When the ridge

ends, it is called termination, while when it dives into two ridges, it is called

bifurcation. The local features are used in fingerprint identification.

A fingerprint authentication system is a pattern recognition system that

recognizes the person based on their fingerprint feature. The two main components

of the system are enrollment and verification/identification as illustrated in Figure

‎1.2. The enrollment stage is responsible for registering the person in the fingerprint

database. During this phase, the fingerprint image is enhanced for fingerprint’s

feature extraction, which is called template. The system can be categorized as:

3

i. A verification system, which authenticates the identity of a person by

comparing the fingerprint feature with the previously stored template.

It determines the identity by conducting a one-to-one comparison and

it either accept or reject the identity (Am I whom I claim I am?).

ii. An identification system that conducts one-to-many comparisons on

the previously stored templates to establish the identity. The

authentication result is either positive or negative (Who am I).

In reality, no fingerprint recognition system can give an unconditional

response about‎the‎individual’s‎identity;‎instead,‎it‎provides‎the‎individual’s‎identity‎

with a matching score ranging in the interval [0,1]. When the matching score is

closer to 1 in the system, then it is most likely that the two fingerprints come from

the same finger. If the matching score is closer to 0 in the system, then it is most

certain that the two fingerprints are not belongs to the same finger. The system is

actually synchronized by threshold t. Two fingerprints are considered the same,

when the matching score is higher than or equal to the threshold t. When the

matching score is less than the threshold t, the two fingerprints are considered

different.

Database

Fingerprint acquisition

Enhancement

Pre-proccessing

Feature selection &

extraction

Fingerprint matching

Enrollment

Verification

Figure ‎1.2 Block diagram of a fingerprint verification system

4

1.2 Background of Research

Fingerprint verification is a very difficult task, mainly due to the degraded

image of fingerprint. The main factors that responsible for this degradation are:

displacement, rotation, partial overlap, non-liner distortion, variable pressure, change

of skin condition, noise, and feature extraction errors. As a result, fingerprints from

different finger may appear similar while fingerprints from the same fingers look

different. (Maltoni et al., 2003).

Fingerprint image matching is performed by, firstly, aligning the images

based on minutiae or reference point then secondly, comparing the images based on

their features. Minutiae matching is a well-known and widely used method for

fingerprint matching. However, minutiae based alignment does not perform well in

case of small fingerprint that has very few minutiae, on the other hand, several

fingerprint matching algorithms are pre-aligned the fingerprint images according to a

reference point. Reference point alignment has the advantage that, once the reference

point is detected, alignment is relatively simple. Due to the displacement and rotation

of the fingerprint on the image, using the center of the image as a reference point,

will give an inaccurate result. Most of the fingerprint matching methods use the

fingerprint singular point as a reference point. It is a region where the ridge curvature

is higher than normal (Maltoni et al., 2003). A lot of researches have been

conducting on detecting the singular point. The existence methods used to detect the

singularity is not efficient enough to discriminate the true singular point from the

spurious detection which is caused by the noise and the results are not accurate.

Fingerprint images as stated above are affected by non-liner distortion,

displacement and rotation. Thus, it is a difficult and challenges task to compare two

fingerprints. During acquisition process the same finger may be placed at different

5

location on the sensor resulting in translation of the fingerprint area; the same finger

may be rotated at different angle. A lot of researches have been done to overcome

these problems. For example, Nanni and Lumini (2006) proposed a rotation of the

original image by an angle of 11.25 and -11.25 then extracting a circle region of

interest around the reference point. The result was not promising. The major issues

that still arising are size of the region of interest, shape of the region of interest,

rotational angle and the rotation applied on the original image or on the region of

interest.

Generally, methods for extracting and matching fingerprint features can be

classified into three categories; minutiae-based, correlation-based, and hybrid

(Maltoni et al., 2003). Minutiae-based techniques usually attempt to align two sets of

minutiae points from two fingerprints and count the total number of matched

minutia. Performance of these techniques, however, relies on the accurate detection

of minutiae points as well as the use of sophisticated matching techniques to compare

two minutiae fields that undergo non-rigid transformations. In the correlation-based

approach, global patterns of ridges and furrows are compared to determine whether

two fingerprints are aligned. Performance of correlation-based techniques is

normally affected by non-linear distortions and noise presents in the image. The

hybrid method involves local orientation, frequency, ridge shape and texture

information for the extraction of fingerprint features. Meanwhile, the robustness of

hybrid method is affected by the difficulty of detecting all minutiae. In addition, its

computational requirements are very high. Therefore, the fingerprint feature selection

and extraction are challenging tasks.

The primary aim of this thesis is to propose a series of techniques for

fingerprint singular point detection and fingerprint verification that will contribute to

highest accuracy.

6

1.3 Problem Statement

The research problems may be stated as follows:

i. How to accurately detect the singular points (SP)?

ii. What is the efficient size and shape of the region of interest

(ROI)?

iii. What is the best method for fingerprint feature selection?

iv. How to extract the fingerprint features efficiently?

1.4 Objective of Study

In order to fulfill the aim of this thesis, the following objectives are to be

targeted:

1. To propose an efficient method to detect the singular point.

2. To propose an effective size and shape of the region of interest.

3. To propose effective methods of selecting and extracting the

fingerprint features that contains distinctive information.

7

4. To test the accuracy of the proposed methods using the existence

procedures.

5. To compare the result obtained by the proposed methods with the

previous methods.

1.5 Scope of Study

This thesis will focus on the use of gray scale fingerprint images obtained

from the public domain FVC2002 database (BSL, 2002). The speed of the proposed

method is not a major concern at this time.

This thesis involves the complete process of fingerprint identification starting

from fingerprint enhancement, singular point detection, region of interest extraction

and normalization, feature extraction, statistical analysis and fingerprint matching. A

one-to-many technique (1-M) during the matching stage was used.

The proposed methods will be analyzed twice, first by using the Second

International Competition for Fingerprint Verification Algorithms (FVC2002) testing

protocol, secondly by using the Program for Rate Estimation and Statistical

Summaries (PRESS), as explained in chapter 4.

PRESS is a tool to help researchers to analyze their collected data on

biometric authentication devices. It has been created through generous funding from

8

the Center for Identification Technology Research (CITeR) and from St. Lawrence

University. PRESS is designed to simplify the analysis of bio-authentication data by

making it easy for the user to create many of the basic statistical summaries in

common usage. These include: confidence intervals, genuine vs. imposter

histograms, and ROC curves. It has been designed by Dr. Michael Schuckers (St.

Lawrence University) and coded by Nona Mramba (University of Maryland) and C.

J. Knickerbocker (St. Lawrence University). The version used in this thesis is a free

research version with no support, however its documentation is available to non-

CITeR members.(Schuckers and Knickerbocker, 2004).

1.6 Thesis Contributions

The thesis contributions are summarized as follows:

i. Singular point detection based on the orientation field reliability.

ii. Selecting the ultimate region of interest based on experiments.

iii. Fingerprint feature selection based on co-occurrence matrix.

iv. Fingerprint feature extraction based on statistical analysis of the gray

level co-occurrence matrix.

9

1.7 Thesis Outline

The following chapter will provide a general review of locating the singular

point, the fingerprint verification systems image-based method.

Chapter 3 presents the proposed techniques for fingerprint verification system

based on the statistical analysis of the gray level co-occurrence matrix.

Chapter 4 presents the experimental results and discussion of the singular

point detection and the fingerprint verification.

Chapter 5 presents conclusions and future work.

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