DUAL MODEL FOR BIOMETRIC IDENTIFICATION...
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DUAL MODEL FOR BIOMETRIC IDENTIFICATION SYSTEM ALA ABDULHAKIM ABDULAZIZ UNIVERSITI TEKNOLOGI MALAYSIA
Transcript of DUAL MODEL FOR BIOMETRIC IDENTIFICATION...
DUAL MODEL FOR BIOMETRIC IDENTIFICATION SYSTEM
ALA ABDULHAKIM ABDULAZIZ
UNIVERSITI TEKNOLOGI MALAYSIA
UNIVERSITI TEKNOLOGI MALAYSIA
DECLARATION OF THESIS / UNDERGRADUATE PROJECT PAPER AND COPYRIGHT
Author‟s full name : ALA ABDULHAKIM ABDULAZIZ
Date of birth : 21/08/1986
Title : DUAL MODEL FOR BIOMETRIC IDENTIFICATION SYSTEM
Academic Session : 1, 2011/2012
I declare that this thesis is classified as :
CONFIDENTIAL (Contains confidential information under the Official Secret
Act 1972)*
RESTRICTED (Contains restricted information as specified by the
organization where research was done)*
OPEN ACCESS I agree that my thesis to be published as online open access
(full text)
I acknowledged that Universiti Teknologi Malaysia reserves the right as follows:
1. The thesis is the property of Universiti Teknologi Malaysia.
2. The Library of Universiti Teknologi Malaysia has the right to make copies for the Purpose
of research only.
3. The Library has the right to make copies of the thesis for academic exchange.
Certified by :
SIGNATURE SIGNATURE OF SUPERVISOR
01663069 Prof. Dr. Azizah Abd Manaf
(NEW IC NO. /PASSPORT NO.) NAME OF SUPERVISOR
Date : Date :
“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 Master of Computer Science (Information Security).”
Signature : ________________________
Name of Supervisor : Prof. Dr. Azizah Abd Manaf
Date : 3rd
January, 2012
DUAL MODEL FOR BIOMETRIC IDENTIFICATION SYSTEM
ALA ABDULHAKIM ABDULAZIZ
A thesis submitted in partial fulfillment of the
requirements for the award of the degree of
Master of Computer Science (Information Security)
Faculty of Computer Science and Information System
Universiti Teknologi Malaysia
JANUARY 2012
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I declare that this thesis entitled “Dual Model for Biometric Identification System” is
the result of my own research except as cited in references. The thesis has not been
accepted for any degree and is not concurrently submitted in candidature of any other
degree.
Signature
Name
Date
:
:
:
ALA ABDULHAKIM ABDULAZIZ
03 JANUARY 2012
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ACKNOWLEDGEMENT
First and foremost, I would like to express my utmost gratitude and
appreciation to my supervisor, Prof. Dr. Azizah Bt Abdul Manaf. Because of her
dedicated, and willing to help me, and willing to accept to become my supervisor.
Also for her excellent guidance outstanding support, And for being patient with what
I did and to advice me, on how I should proceed in this project.
Special thanks to Associate Professor Dr. Zuraini Ismail and Associate
Professor Wardah binti Zainal Abidin, Dr. Bharanidharan Shanmugam and Dr. Mazdak
Zamani for their comments that make this study more comprehensive and precious. I
would like to extend my thanks to staff at UTM AIS for directly or indirectly
extending their help during the work.
I also take this opportunity to give thanks for Wu zhili, Libor Masek,
Manvjeet Kaur and Mukhwinder Sing for their fingerprint and iris algorithms
implementation coding.
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ABSTRACT
A biometric system which relies only on a single biometric identifier in
making a personal identification is often not able to meet the desired performance
requirements. Dual modal has great demands to overcome the issue involved in
single biometric identifier. The use of personal identity verification systems with a
serial architecture dual-modal biometrics has been proposed using fingerprint and iris
pattern in order to increase the performance and security against environmental
variations and fraudulent. The dual modal system is based on an empirical analysis
of the fingerprint and iris images and it is split in several steps using local image
properties. The fingerprint Minutiae extraction steps are loading the finger image,
enhancement by histogram equalization and Fourier transform, binarization,
segmentation by block direction estimation and region of interest extraction by
morphological operations, remove H-break, remove spur, extract minutia and remove
false minutia. At the same time, the iris system steps are capturing iris patterns,
determine the location of the iris boundaries, converting the iris boundary to the
stretched polar coordinate system, extracting the iris code. The dual modal system is
achieved at the fingerprint matching score level less than 75%. The implementation
of methods, algorithms and the graphical user interface was done by using MATLAB
and CASIA database of 11 samples of fingerprint and iris data. Statistical
Experimental used in this project on a small sample size, which is very difficult to
conduct a full analysis of the observed results and consider as a main limitation of
dual modal system. Future work can be done by performing the statistical
experiments on a larger sample size, and conduct a full analysis of the observed
results.
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ABSTRAK
Sistem biometrik yang bergantung hanya pada satu identifikasi biometrik
dalam membuat pengenalan peribadi sering tidak mampu untuk memenuhi keperluan
prestasi yang diingini. Ragaman Dual mempunyai permintaan besar untuk mengatasi
isu yang terlibat dalam pengecam biometrik tunggal. Penggunaan sistem pengesahan
identiti peribadi dengan dwi-mod biometrik telah dicadangkan dengan senibina
bersiri menggunakan cap jari dan corak iris untuk meningkatkan prestasi dan
keselamatan terhadap perubahan alam sekitar dan penipuan. Sistem dua ragaman
adalah berdasarkan analisis empirik cap jari dan imej iris dan berpecah dalam
beberapa langkah yang menggunakan ciri-ciri imej tempatan. Langkah-langkah
pengekstrakan detel cap jari memuatkan imej jari, peningkatan oleh penyamaan
histogram dan jelmaan Fourier, binarization, segmentasi oleh anggaran arah blok dan
rantau pengekstrakan kepentingan oleh operasi morfologi, mengeluarkan H-rehat,
keluarkan merangsang, cabutan minutia dan keluarkan minutia palsu. Pada masa
yang sama, langkah-langkah sistem iris menangkap corak iris, menentukan lokasi
sempadan iris, mengubah sempadan iris kepada sistem koordinat kutub diregangkan,
mengekstrak kod iris. Sistem dua mod dicapai pada tahap cap jari skor yang hampir
sama kurang daripada 75%. Pelaksanaan kaedah, algoritma dan antara muka
pengguna grafik yang telah dilakukan dengan menggunakan pangkalan data
MATLAB dan CASIA 11 sampel cap jari dan data iris.Dari keputusan permerhatian
yang dilakukan, uji kaji statistik ke atas saiz sampel yang kecil, adalah sukar untuk
menganalisa keputusan yang diperolehi dan ianya merupakan keterbatasan ke atas
ragaman dua sistem ini. Pada masa hadapan, diharap sampel uji kaji yang lebih besar
dan analisa ke atas hasil keputusan dari pemerhatian dapat dijalankan dengan
sepenuhnya.
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TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
ACKNOWLEDGEMENT iii
ABSTRACT iv
ABSTRAK v
TABLE OF CONTENTS vi
LIST OF TABLES xi
LIST OF FIGURES xii
LIST OF ABBREVIATION
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1 INTRODUCTION 1
1.1 Introduction 1
1.2 Overview of biometrics 2
1.2.1 Types of Biometrics 2
1.2.2 Modes by a biometric system 3
1.3 Background of the Problem 4
1.4 Problem Statement 7
1.5 Research Questions 8
1.6 Objectives 8
1.7 Project Aim 9
1.8 Project Scope 9
1.9 Project Requirements 9
1.10 Summary
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2 LITERATURE REVIEW 11
2.1 Introduction 11
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2.2 Biometric System 11
2.3 Overview of Biometrics Technology 13
2.4 Properties of Biometric Systems 17
2.5 Fingerprint Recognition 19
2.5.1 Minutiae-based algorithm 21
2.6 Iris Recognition 24
2.6.1 Iris Recognition Process 24
2.7 Multimodal Biometrics 29
2.8 Some Fingerprint – Face Fusion related work 32
2.9 Some Voice – Face Fusion related work 35
2.10 Some Fingerprint – Iris Fusion related work 37
2.10.1 The proposed algorithms 38
2.11 Summary
39
3 DEVELOPMENT METHODOLOGIES 40
3.1 Introduction 40
3.2 Research Operational Framework 40
3.3 Review Fingerprint and Iris Algorithms 41
3.4 Design dual model 42
3.5 Design Fingerprint 44
3.5.1 Image pre-processing 44
3.5.2 Minutiae feature extraction 45
3.5.2.1 Enhancement 46
3.5.2.2 Binarization 47
3.5.2.3 Segmentation 47
3.5.2.4 Thinning 50
3.5.2.5 Minutia marking 50
3.5.2.6 False Minutia removal 52
3.5.3 Matching 53
3.5.3.1 Alignment stage 54
3.5.3.2 Match stage 55
3.6 Design Iris 56
3.6.1 Acquisition 57
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3.6.2 Iris localization 57
3.6.3 Feature Extraction 57
3.6.3.1 Localization 57
3.6.3.2 Normalization 58
3.6.3.3 Feature encoding 59
3.6.4 Matching 60
3.7 Implementation of the algorithms 61
3.8 Testing the system 61
3.9 Summary of Deliverable 61
3.10 Summary
63
4 SYSTEM ANALYSIS AND DESIGN 64
4.1 Introduction 64
4.2 System Analysis 65
4.2.1 Requirements Models 65
4.2.1.1 Formal Requirements 65
4.2.1.2 Non-Functional Requirements 68
4.2.2 Use Cases 70
4.2.3 Sequence Diagram 74
4.2.4 Activity Diagram 80
4.3 System Design 81
4.3. 1 Data Model 82
4.3.2 Data dictionary 83
4.3.3 User Interface 83
4.4 Summary
87
5 SYSTEM IMPLMENTATION 88
5.1 Introduction 88
5.2 Tools and techniques 88
5.2.1 Windows XP and above 88
5.2.2 Matlab 89
5.2.3 Wamp Server 89
5.2.4 MYSQL 90
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5.3 Database Implementation 90
5.3.1 Create database 91
5.3.2 Create Table 91
5.4 Fingerprint Implementation 92
5.4.1 Type command start_GUI_single_mode 93
5.4.2 Click Load Button 94
5.4.3 Click his-Equalization Button 95
5.4.4 Click FFT Button 96
5.4.5 Click Direction Button 97
5.4.6 Click ROI Area Button 98
5.4.7 Click Real Minutia Button 99
5.4.8 Click Save Button 100
5.5 Iris implementation 101
5.6 Dual Modal Implementation 103
5.6.1 Main Dual Modal System 104
5.6.2 Admin Login 104
5.6.3 Fingerprint Recognition 105
5.6.4 Admin Menu 106
5.6.5 Add New User 107
5.6.6 Add New User 108
5.6.7 Save Iris 109
5.6.8 User Menu 109
5.6.9 Fingerprint Matching Menu 110
5.6.10 Iris Matching Menu 110
5.7 Summary
111
6 RESULT AND DISCUSSION 112
6.1 Introduction 112
6.2 Recognition of Individuals based on Iris 112
6.3 Recognition of Individuals based on Fingerprint 116
6.3.1 Experimentation Results 121
6.4. Recognition of Individuals based on dual modal 122
6.4.1 Experimentation Results
123
6.5 Findings 124
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6.5.1 Importance finding 124
6.5.2 Limitations 125
6.6 Summary
125
7 CONCLUSION 126
7.1 Introduction 126
7.2 Summary of Findings and Conclusion 127
7.2.1 Examine the multi-modal biometric
identification system.
127
7.2.2 Developing dual model biometric system by
using serial architecture.
128
7.2.3 Test dual modal biometric identification
system.
128
7.3 Recommendations 129
7.4 Contribution to knowledge
129
REFRENCES 130
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LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Comparison between passwords vs token vs biometrics 13
2.2 Comparison of biometric technologies 18
2.3 Comparison between Minutiae vs Correlation vs Ridge 20
3.1 Operational Framework 62
4.1 Manage User 66
4.2 Manage Dual Modal 67
4.3 Extensibility 69
4.4 Reliability 69
4.5 Security 70
4.6 Data Dictionary 83
6.1 Matching Rate 123
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LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 a) Different minutiae type (b) Ridge ending & Bifurcation 21
2.2 Fingerprint recognition system 21
2.3 Iris identification process 25
2.4 Serial integration 30
2.5 Parallel integration 30
2.6 Binary Classification Approach 32
2.7 Fingerprint – Face Fusion based Identification System 33
2.8 Voice – Face Fusion based Identification System 35
2.9 Fingerprint – Iris Fusion based Identification System 38
3.1 Research Operational Framework 41
3.2 Dual Model architecture 42
3.3 Dual Model Design 43
3.4 Fingerprint Design 44
3.5 Preprocessing 45
3.6 Histogram 46
3.7 ROI 48
3.8 Minutiae Extraction 49
3.9 Minutiae Marking 50
3.10 Post Processing 51
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3.11 False Minutia Structures 52
3.12 Iris Design 56
3.13 Unwrapping the iris 59
3.14 ID Gabor wavelets 60
4.1 Formal Requirements 65
4.2 Manage Users Requirements 66
4.3 Manage Dual Modal System 67
4.4 Non-Functional Requirements 68
4.5 Extensibility Requirements 68
4.6 Reliability Requirements 69
4.7 Security Requirements 70
4.8 Main Use Case 71
4.9 Fingerprint Use Case 72
4.10 Iris Use Case 73
4.11 User use Case 73
4.12 Admin login Sequence Diagram 74
4.13 Admin Add User Sequence Diagram 75
4.14 Admin Delete User Sequence Diagram 76
4.15 Admin Update User Sequence Diagram 77
4.16 Fingerprint Sequence Diagram 78
4.17 Admin Iris Sequence Diagram 79
4.18 Admin activity Diagram 80
4.19 User activity Diagram 81
4.20 Data Model Diagram 82
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4.21 Daul modal Interface 84
4.22 Main Interface 85
4.23 Admin Interface 86
4.24 User Interface 87
5.1 Create Database 91
5.2 Create Table 91
5.3 Admin Table 92
5.4 User Table 92
5.5 Fingerprint Recognition System 93
5.6 Load a gray level fingerprint image 94
5.7 Histogram Equalization 95
5.8 „FFT‟ button 96
5.9 direction button 97
5.10 ROI extraction (right) 98
5.11 Minutia Marking (right) and False Minutia Removal 99
5.12 Save minutia to a text file 100
5.13 Original Image 101
5.14 Segmented Iris Image 101
5.15 Noised Segmented Iris Image 102
5.16 Normalized Iris Image 102
5.17 Polar Form of Iris Image 102
5.18 Output of the Iris Image 103
5.19 Main GUI 104
5.20 Admin login 104
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5.21 Fingerprint Recognition System 105
5.22 Admin Menu 106
5.23 Add New User Menu 107
5.24 save fingerprint for new user 108
5.25 Save iris for new user 109
5.26 User access menu 109
5.27 Fingerprint Matching Menu 110
5.28 Iris Matching Menu 110
6.1 Inter-class and Intra-class Distributions 114
6.2 Hamming Distance Distributions 115
6.3 Hamming Distance Experiment Result 116
6.4 Minutia Extractor Process 116
6.5 Original fingerprint image 117
6.6 Histogram after the Histogram Equalization 117
6.7 Original Image (Left). Enhanced image (Right) 118
6.8 Fingerprint enhancement by FFT Enhanced image 118
6.9 The Fingerprint image after adaptive binarization 119
6.10 Direction map 119
6.11 Region of Interest ROI 120
6.12 Thinned Images 120
6.13 Minutia Extractions 121
6.14 FAR and FRR curve 122
6.15 Dual modal recognition 123
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LIST OF ABBREVIATION
FAR - False Acceptance Rate
FRR - False Rejection Rate
PIN - Personal Identification Number
FFT - Fast Fourier Transform
MLP - Multilayer Perception
ROI - Region of Interest
HD - Hamming Distance
UML - Unified Modeling Language
Matlab - Matrix Laboratory
D - Distance Measured in Standard Deviations
µs - Mean of the Intra-class Distribution
µD - Mean of the Inter-class Distribution
ɕs - Standard Deviation of the Intra-class
ɕD - Inter-class Distributions
CHAPTER 1
INTRODUCTION
1.1 Introduction
Software and computer systems are recognized as a subset of simulated
intelligent behaviors of human beings described by programmed instructive
information. Biometric information system is one of the finest examples of computer
system that tries to imitate the decisions that humans make in their everyday life,
specifically concerning people identification and matching tasks. A biometric is a
biological measurement of any human physiological or behavior characteristics that
can be used to verify the identity of an individual (Wang, 2007).
The evolvements in science and technology have made it possible to use
biometrics in applications where it is necessary to establish or verify the identity of
individuals. Applications such as passenger control in airports, access control in
restricted areas, border control, database access and financial services are some of the
examples where the biometric technology has been applied for more reliable
identification and verification.
For biometric applications that demand robustness and accuracy higher than
that provided by any single biometric trait, Dual model biometric approaches often
provide promising results. Dual model Biometric Authentication or Dual model
Biometrics is the approach of using two biometric traits from a single user in an
effort to improve the results of the authentication process.
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In this project the researcher will develop dual model Biometrics
identification system by using the fingerprint and iris recognition system. This
system will provide robustness, accuracy, reliability, security and stability of the
system over its individual unimodal components.
1.2 Overview of biometrics
A biometric provides automatic recognition of an individual based on his/her
physical or behavior characteristics. Biometric offer several advantage over using ID cards
or PIN numbers. There are so many advantages of the biometrics such as no need to
memorize passwords, requires physical presence of the person to be identified , cannot be
borrowed, stolen, or forgotten, cannot leave it at home and better than password/PIN or
smart cards. Biometric systems have been developed based on facial features, voice, hand
geometry, handwriting, the retina and the one presented in this thesis, fingerprints and iris.
1.2.1 Types of Biometrics
Biometric characteristics can be separated into two types:
i. Physiological characteristics
A biometric characteristic based primarily on an anatomical or biological
characteristic, rather than a learned behavior. They are related to the shape of the
body. The trait that has been used the longest, for over one hundred years, are
fingerprints; other examples are face recognition, hand geometry and iris recognition
(Ashbourn et al, 2004).
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ii. Behavioral characteristics
A biometric characteristic that is learned and acquired over time rather than
one based primarily on biology. They are related to the behavior of a person. The
first characteristic to be used that is still widely used today is the signature, keystroke
and voice (Ashbourn et al, 2004).
1.2.2 Modes by a biometric system
The biometric system has divided into two modes which are general
biometric system and biometric system application. The two biometrics systems are
explained briefly as follow:
i. General biometric system
Biometric system is an automated system capable of Capturing of biometric
sample from an end user. Biometric system is also extracting and processing the
biometric data from that sample. Furthermore, the biometric system is storing the
extracted information in a database then comparing the biometric data with data
contained in one or more references. Finally the biometric system is deciding how
well they match and indicating whether or not an identification or verification of
identity has been achieved (Jain etc al, 2004).
ii. Biometrics system application
Biometrics system has two type of application which are identification and
verfication. However, the identification is a task where the biometric system searches
a database for a reference matching a submitted biometric sample, and if found,
returns a corresponding identity.in another hand, the verification is a task where the
biometric system attempts to confirm an individual‟s claimed identity by comparing
a submitted sample (biometric data) to one or more previously enrolled templates.
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the section below is explain more about the two types of biometrics system (Jain etc
al, 2004).
a. Identification
The identification method is selecting the correct identity of an unknown
person from a database of registered identities. Furthermore, it is one too many
matching process, because the system is asked to complete a comparison between the
person‟s biometrics and all the biometric templates stored in a database. The system
can take either the best match, or it can score the possible matches, and rank them in
order of similarity (Jain etc al, 2004).
b. Verification
The verification method is verifying whether a person is who he or she claims
to be. In another hand , It is one to one matching process, as the system has to
complete a comparison between the person‟s biometric and only one chosen template
stored in a centralized or a distributed database, e.g. directly on a chip for an identity
document. Such a method is applied when the goal is to secure and restrict specific
accesses with obviously cooperative users (Jain etc al, 2004).
1.3 Background of the Problem
With the rapid spread of information technologies, data processing and
electronic transactions, strong need for new identification and verification practices
has emerged. In today world a wide variety of applications requires reliable and
secure authentication methods to confirm the identity of an individual requesting
their service. In recent years, biometric authentication has seen considerable
improvements in reliability and accuracy, with some biometrics offering reasonably
good overall performance. The increasing demand of enhanced security systems has
led to an unprecedented interest in biometric based person authentication system.
Biometric systems based on single source of information are called unimodal
systems.
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i. Problem of single biometric System
Most of the biometric systems deployed in real world applications are
unimodal which rely on the evidence of single source of information for
authentication (e.g. fingerprint, face, iris, voice etc.). These systems are vulnerable to
variety of problems such as noisy data, intra-class variations, inter-class similarities,
non-universality and spoofing (Mane and Jadhav, 2009). It leads to considerably high
false acceptance rate (FAR) and false rejection rate (FRR), limited discrimination
capability, upper bound in performance and lack of permanence (Mane and Jadhav,
2009). However, even the new biometrics is still facing numerous problems, some of
them inherent to the technology itself. In particular, biometric authentication systems
generally suffer from enrollment problems due to non-universal biometric traits,
susceptibility to biometric spoofing or insufficient accuracy caused by noisy data
acquisition in certain environments (Mane and Jadhav, 2009).
Hence, single biometric may not be able to achieve the desired performance
requirement in real world applications. Furthermore, unimodal biometric systems
rely on the evidence of only a single biometric trait. The problem that has been faced
from a single biometric it has disadvantages regarding accuracy and performance
(Maltoni and Cappelli, 2009). Furthermore, Spoofing of biometric data is still facing
numerous problems since it is far easier to spoof only one biometric trait (Maltoni
and Cappelli, 2009).
However, each biometric technology has its strengths and limitations, and no
single biometric is expected to effectively satisfy the requirements of all verification
or identification applications. A single biometric sometimes fails to be accurate
enough for the identification of a large user population. Another disadvantage of
using only one biometric is that the physical characteristics of a person for the
selected biometric might not be always available or readable (Ko, 2005). Biometric
systems based on one (one-modal) biometric are often not able to meet the desired
performance requirements, and have to contend with a variety of problems such as
noisy data, intra-class variations, restricted degree of freedom, non-university, spoof
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attacks for example finger print spoofing with rubber and unacceptable error rates
(Ko, 2005).
ii. Problem of Fingerprint System
Fingerprint recognition is still a complex and very challenging pattern
recognition task, designing algorithms capable of extracting effective features and
matching them in a robust way is very hard, especially in poor quality fingerprint
images and when low-cost acquisition devices with a small area are adopted. One of
the open issues in matching two fingerprint samples of the same finger is how to deal
with the non-linear distortions, often produced by an incorrect finger placement of
the finger over the sensing element, which make a global rigid comparison unfeasible
(Maltoni and Cappelli, 2009).
One of the challenges of fingerprint technology is individuals that have
poorly defined (or tenuous) ridges in their fingerprints. Furthermore, since the finger
actually touches the scanning device, the surface can become oily and cloudy after
repeated use and reduce the sensitivity and reliability of optical scanners.
Furthermore, Fingerprints can be easily forged from touched surfaces and can be
copied in a small amount of time using readily available materials (Kumar and Ryu,
2009).
iii. Problem of Iris System
Another type of access control deals with biometric information is iris
biometrics. The iris system has some of the good aspect, but also has some of the
disadvantage. The first advantage if the iris is uniqueness since every person has his
own iris so it is impossible that two people has the same iris, even the twins don‟t
have the same iris as well. The second advantage of iris is performance since the
FAR AND FFR are very low because the scan s very accurate (Cavadini, et al 2008).
At the same time iris has some disadvantages. One of the disadvantage of iris
recognition is the acceptability is very low, even if with modern technology an iris
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could be photographed from 10cm to few meters away. Moreover, the second
disadvantages of the iris if a person does not want to cooperate the iris reading
procedure becomes very difficult or the image quality influences the FRR (Cavadini,
et al 2008).
1.4 Problem Statement
With the rapid spread of information technologies, data processing and
electronic transactions, strong need for new identification and verification practices
has emerged. The problem that has been faced from a single biometric sometimes it
fails to be accurate enough for the identification of a large user population (Mane and
Jadhav, 2009). Furthermore, single biometric has variety of problems such as noisy
data, intra-class variations, inter-class similarities, non-universality and spoofing
(Mane and Jadhav, 2009). Hence, single biometric may not be able to achieve the
desired performance requirement in real world applications (Mane and Jadhav,
2009).
Fingerprint is the newest and most rapidly-developing field of access control
deals with biometric information. Since the finger actually touches the scanning
device, the surface can become oily and cloudy after repeated use and reduce the
sensitivity and reliability of optical scanners. Furthermore, fingerprints can be easily
forged from touched surfaces and can be copied in a small amount of time using
readily available materials (Kumar and Ryu 2009). Fingerprints can be affected by
dirt, dryness and scarring (Maltoni and Cappelli, 2009).
Another type of access control deals with biometric information is iris
biometrics. One of the disadvantage of iris recognition is the acceptability is very
low, even if with modern technology an iris could be photographed from 10cm to
few meters away. Moreover, the second disadvantages of the iris if a person doesn‟t
want to cooperate the iris reading procedure becomes very difficult or the image
quality influences the FRR (Cavadini et al, 2008).
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1.5 Research Questions
To develop a dual model biometric identification system it is important to
know the following:
i. What are the multimodal biometric and the current research on biometrics?
ii. How can we develop the dual modals of biometrics to achieve reliable
biometric system?
iii. How can we make sure the proposed model is working well?
First question answered in Literature review chapter, the rest of the questions
which the first one regard how we can develop the dual modals of biometrics to
achieve reliable biometric system the answer of it will be in proposed dual model for
this project. And the question about How can we make sure the proposed model is
working well the answer of it will be after implementing the proposed dual model
then the researcher will perform testing.
1.6 Objectives
The objectives of this project are as follows:
i. To examine the multi-modal biometric identification system.
ii. To develop dual model biometric identification system by using serial
architecture.
iii. To test dual modal biometric identification system.
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1.7 Project Aim
The aim of this project is to examine on the multi-model biometric
identification system. That offers reliability by using the fingerprint and iris
recognition system. Furthermore, the second aim will be to create an open-source
fingerprint and iris recognition system in order to verify the claimed performance of
the technology. The development tool used will be MATLAB and use the well
known database is the CASIA Iris Image Database (version 1.0) provided by the
Chinese Academy of Sciences and UBIRIS Database. Finally, the last aim is to test
and evaluate the fingerprint and iris recognition system. This project will tested using
on CASIA database of grayscale eye and fingerprint images in order to verify the
claimed performance of finger and iris recognition technology.
1.8 Project Scope
This study concerns on the nature of multi-model biometric in terms of its
algorithm, techniques, methods and it is effectiveness. This project will also look at
the different types of algorithm and techniques which have been implemented in
fingerprint and iris. This project will be focus on various algorithm and techniques
that have been utilized in order to impalement fingerprint and iris. In this project the
researcher going to implement the fingerprint and iris based identification system for
access control. However the implementation of methods, algorithms and the
graphical user interface will be done using MATLAB and CASIA database of
grayscale eye and fingerprint images in order to verify the claimed performance of
finger and iris recognition technology.
10
1.9 Project Requirements
As long as the implementation of this project will be done by using
MATLLab, the MATLAB tool is required in order to design and implement of
methods, algorithms and the graphical user interface of this project as well as trying
to verify the project is matching the objectives which this project has been designed
aiming to achieve those objectives. In this project the researcher will use CASIA
database of grayscale eye and fingerprint images in order to verify the claimed
performance of finger and iris recognition technology.
1.10 Summary
This chapter provides a background on biometrics. The concept of biometrics
is defined an automatic recognition of an individual based on his/her physical or
behavior characteristics. Most of the biometric systems deployed in real world
applications are unimodal which rely on the evidence of single source of information
for authentication (e.g. fingerprint, face, iris, voice etc.). These systems are
vulnerable to variety of problems such as noisy data, intra-class variations, inter-class
similarities, non-universality and spoofing. A quick overview about the biometrics
technique is clarified. The problem statement is declared and the objectives of this
project are stated.
131
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