DEVELOPMENT OF AN IRIS AUTHENTICATION BY UMME …

DEVELOPMENT OF AN IRIS AUTHENTICATIONALGORITHM FOR PERSONAL IDENTIFICATION

BY

UMME TAHMINA TANIA

A thesis submitted in fulfilment of the requirement for thedegree of Master of Science (Computer and Information

Engineering)

Kulliyah of EngineeringInternational Islamic University Malaysia

MAY 2015

ii

ABSTRACT

Biometric systems differentiate people based on their uniquely characteristics manner.Among various biometric systems, iris recognition provides most reliableidentification. In recent years, the development and practice of the field of irisrecognition has expanded dramatically. Now it becomes a practical area of scienceand technology. The developments of core algorithm increase its practicalapplications. The research regarding iris recognition is not only focusing on idealimage where camera uses infrared illumination but also focusing on non-ideal imagewhich has been taken in presence of visible lighting. It takes lot of user cooperation tocapture an ideal image which makes the system time consuming. To make the systemmore user friendly, the algorithm to handle non-ideal image is essential. The main aimof this research work is to develop an algorithm which can locate iris from both idealimage and non-ideal image. Three major steps of the iris recognition system arelocalization of iris, normalization of iris and feature extraction of iris. The HoughTransform and image thresholding technique has been applied to localize iris in agiven eye image. The Hough Transform shows excellent performance to localize irisin an ideal image. However, Hough Transform fails to perform accurate localizationfor non-ideal image. On the other hand, image thresholding techniques show relativelygood performance for both ideal and non-ideal image. The isolated iris region is thentransformed from Cartesian to polar form by using Daugman intrego differentialoperator. Finally to encode the feature into a binary template 1D Log-Gabor filter hasbeen used. A simple Boolean Exclusive-OR operator (XOR) function has been appliedto check whether two binary templates are from same image or not. To validate theperformance of the algorithm both ideal and non-ideal eye images have been used.Image from CASIA Iris Interval database has been used to validate the performance ofalgorithms for ideal image and image from UBIRIS database has been used to validatethe performance of algorithms for non-ideal image. On a set of 138 differentcombinations, the algorithm shows 0% false acceptance rate. However, observation on94 same class variations shows 4.25% false rejection rate. Therefore, the irisrecognition algorithm proves to be a consistent and precise biometric technology.

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خلاصة البحث

یمیّز نظام القیاس الحیوي بین الناس استناداً إلى أنساق الخصائص الفریدة لھم. وضمن تنوّع أنظمة القیاس في السنوات الأخیرة، تم التوسّع ر الطرق موثوقیة في تحدید الھویة. قزحیة العین أكثعلى الحیوي، یعد التعرّف

في أشكال التطویر والتطبیق لمجال التعرّف على القزحیة حتى أصبحت الآن مساحة عملیة في العلوم رّف على والتكنولوجیا، وكان تطوّر الخوارزمیة الأساسیة سبباً لاتساع تطبیقاتھ العملیة. إن البحوث المتعلقة بالتع

القزحیة لا تركّز فقط على الصور المثالیة حیث تستخدم الكامیرا إضاءة الأشعة تحت الحمراء، ولكن تركز أیضا على الصور غیر المثالیة والتي أخذت في الإضاءة المرئیة. ولأن التقاط صورة مثالیة یحتاج الكثیر من التعاون

، أصبحت خوارزمیة أكثر ملاءمة للمستخدمینولجعل النظامطویلا، وقت النظاما یجعلمن قِبل المستخدم ممتتمكن خوارزمیةھو تطویرھذا العمل البحثيلتعامل مع الصور غیر المثالیة ضروریة. إن الھدف الرئیسي منا

الخطوات الثلاث الرئیسیة .مثالیةمثالیة الإضاءة أو غیرصورة سواء كانتكلفي القزحیةمن تحدید موقعتعرف على القزحیة ھي: تعیین موقع القزحیة، وتطبیع القزحیة، واستخراج سمات القزحیة. تم تطبیق لنظام ال

لتعیین موقع Image Thresholdingو مستوى العتبة للصورة Hough Transformھاف تقنیة تحویل یة في الصورة المثالیة القزحیة في صورة العین المستخدمة. تظھر تقنیة تحویل ھاف أداءً متمیزاً في تعیین القزح

لكنھا فشلت في أداء تعیین دقیق للقزحیة في الصورة غیر المثالیة. ومن جھة أخرى، أظھرت تقنیة مستوى العتبة بعد ذلك تم تحویل منطقة القزحیة المعزولة من للصورة أداءً جیداً نسبیاً مع كل من الصور المثالیة وغیر المثالیة.

). وأخیراً تم استخدام Daugmanintregoالصیغة القطبیة باستخدام العامل التفاضلي (الصیغة الدیكارتیة إلى المقصورالجوازأحادي البُعد لترمیز السمة في قالب ثنائي. وقد تم استخدام دالة مشغّل Log-Gaborفلتر

ا أو لا. وللتأكد من صحة ) للتحقق من أن اثنین من القوالب الثنائیة ھما من الصورة ذاتھXORالمنطقي البولیاني (، وأخرى غیر مثالیة CASIAأداء الخوارزمیة، تم استخدام صور مثالیة من قاعدة بیانات القزحیة المعتمدة

138% على مجموعة من 0. أظھرت الخوارزمیة معدل قبول خاطئة UBIRISالإضاءة من قاعدة البیانات بالتالي تم إثبات . و%4.25فئة ذاتھا أظھر معدل رفض قدره تغییر من ال94تولیفة مختلفة. ومع ذلك، فإن رصد

.ودقیقةّمتسقةھي تكنولوجیا حیویةقزحیة العینالتعرف علىخوارزمیةأن

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APPROVAL PAGE

I certify that I have supervised and read this study and that in my opinion; it conformsto acceptable standards of scholarly presentation and is fully adequate, in scope andquality, as a thesis for the degree of Master of Science in Computer and InformationEngineering.

………………………………….S.M.A. MotakabberSupervisor

.…..……………………………..Muhammad Ibn IbrahimyCo-Supervisor

I certify that I have read this study and that in my opinion it conforms to acceptablestandards of scholarly presentation and is fully adequate, in scope and quality, as athesis for the degree of Master of Science in Computer and Information Engineering.

..………………………………..Rashidah F.OlanrewajuInternal Examiner

..................................................Shohel SayeedExternal Examiner

This thesis was submitted to the Department of Electrical and Computer Engineeringand is accepted as a fulfilment of the requirement for the degree of Master of Sciencein Computer and Information Engineering.

…………………………………Teddy Surya GunawanHead, Department of Electricaland Computer Engineering

This thesis was submitted to the Kulliyyah of Engineering and is accepted asfulfilment of the requirement for the degree of Master of Science in Computer andInformation Engineering.

…………………………………Md. Noor B. SallehDean, Kulliyyah of Engineering

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DECLARATION

I hereby declare that this thesis is the result of my own investigations, except where

otherwise stated. I also declare that it has not been previously or concurrently

submitted as a whole for any other degrees at IIUM or other institutions.

Umme Tahmina Tania

Signature:……………………………… Date:……………………

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INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND AFFIRMATIONOF FAIR USE OF UNPUBLISHED RESEARCH

Copyright © 2015 by International Islamic University Malaysia. All rights reserved.

DEVELOPMENT OF AN IRIS AUTHENTICATIONALGORITHM FOR PERSONAL

IDENTIFICATION

I hereby affirm that The International Islamic University Malaysia (IIUM) holds all

rights in the copyright of this work and henceforth any reproduction or use in any

forms or by means whatsoever is prohibited without the written consent of IIUM. No

part of this unpublished research may be reproduced, stored in a retrieval system, or

transmitted, in any form or by means, electronic, mechanical, photocopying, recording

or otherwise without prior written permission of the copyright holder.

Affirmed by Umme Tahmina Tania

.............................. ................................Signature Date

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ACKNOWLEDGEMENTS

Praise to Allah SWT., the Almighty for bestowing His Grace and Mercy, Solawat andSalam to our beloved prophet (P.B.U.M).

At the very outset, all my prayers and thankfulness to Almighty Allah forabilities in granting me the opportunity with His Great Blessings to carry out andaccomplish this thesis successfully throughout the years of my achievement forseeking the knowledge.

I wish to express my deepest gratitude to my supervisor, Dr. S.M.A.Motakabber for permitting me to carry out this thesis with his guidance. I takeimmense pleasure in expressing my heartfelt gratitude to my co-supervisor, Dr.Muhammad Ibn Ibrahimy as for his inspiration, guidance and valuable assistance inhelping me to complete my research on time.

I extend my thankfulness to all my friends. Their encouragement and helpmade me confident to fulfilling my desire and overcoming every difficultyencountered.

Last but not least, I am very grateful to my mother and my elder sister: UmmeHani for their understanding and their love, encouragement to work hard and tocontinue pursuing my masters. I owe my every achievement to all of them.

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TABLE OF CONTENTS

Abstract….. .......................................................................................................................... iiAbstract in Arabic .............................................................................................................. iiiApproval Page.....................................................................................................................ivDeclaration ...........................................................................................................................vCopyright page....................................................................................................................viDedication ........................................................................................................................ viiAcknowledgement ........................................................................................................... viiiList of Tables ......................................................................................................................xiList of Figures ................................................................................................................... xiiList of Abbreviations ........................................................................................................xivList of Symbols .................................................................................................................xvi

CHAPTER ONE: INTRODUCTION ..............................................................................11.1 Background.........................................................................................................11.2 The Human Iris ...................................................................................................21.3 Iris Recognition ..................................................................................................41.4 Why Iris Recognition..........................................................................................41.5 Problem Statement..............................................................................................51.6 Objectives ...........................................................................................................51.7 Research Methodology .......................................................................................61.8 Scope of the Research.........................................................................................81.9 Organization of the Thesis..................................................................................8

CHAPTER TWO: LITERATURE REVIEW ............................................................92.1 Introdution ..........................................................................................................92.2 Iris Image Acquisitions.....................................................................................102.3 Non-Ideal Images and Quality Metrics.............................................................102.4 Image Compression ..........................................................................................112.5 Iris Segmentation ..............................................................................................122.6 Texture Coding and Matching ..........................................................................14

2.6.1 Experiments Using the CASIA V1 Dataset .............................................142.6.2 “Eigen-Iris” Approaches .........................................................................152.6.3 Alternative Texture Filter Formulations ................................................152.6.4 Alternative Methods of Texture Analysis ..............................................16

2.7 Algorithms That Analyze the Iris in Parts ........................................................172.8 Approaches to Speed Iris Matching..................................................................182.9 Exploiting “Fragile” Bits in the Iris Code ........................................................192.10 Use 0f “Sparse Representation” Techniques ..................................................192.11 Multi-Biometrics Involving the Iris................................................................202.12 Comparison of Traditional Iris Recognition Algorithms ...............................212.13 Summary.........................................................................................................25

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CHAPTER THREE: METHODOLOGY .....................................................................263.1 Introduction.......................................................................................................263.2 Data Set.............................................................................................................27

3.2.1 Chinese Academy of Sciences - Institute Of Automation ......................283.2.2 Multimedia University Database ...........................................................283.2.3 UBIRIS Iris Database ...............................................................................28

3.3 Segmentation ....................................................................................................283.3.1 Hough Transform ...................................................................................293.3.2 Image Thresholding Method..................................................................31

3.4 Normalization ...................................................................................................313.4.1 Daugman’s Rubber Sheet Model ...........................................................32

3.5 Feature Encoding ..............................................................................................353.5.1 Log-Gabor Filter .....................................................................................35

3.6 Matching ...........................................................................................................373.7 Summary...........................................................................................................39

CHAPTER FOUR: RESULTS AND FINDINGS.....................................................404.1 Introduction.......................................................................................................404.2 Segmentation ....................................................................................................40

4.2.1 Overview................................................................................................404.2.2 Result of Iris Segmentation....................................................................404.2.3 Result of Image Thresholding Method...................................................42

4.3 Normalization ...................................................................................................444.3.1 Overview ................................................................................................444.3.2 Result of Iris Normalization...................................................................44

4.4 Feature Encoding ..............................................................................................444.4.1 Overview ................................................................................................444.4.2 Results of Iris Feature Encoding.............................................................45

4.5 Matching ...........................................................................................................454.5.1 Overview ................................................................................................454.5.2 Results of Template Matching ...............................................................45

4.6 Uniqueness of Iris Patterns ...............................................................................484.6.1 Overview ................................................................................................484.6.2 Results of Uniqueness of Iris Patterns ...................................................48

4.7 Recognition of Individuals ...............................................................................694.7.1 Overview ................................................................................................694.7.2 Results of Recognition of Individuals .....................................................69

4.8 Summary...........................................................................................................75

CHAPTER FIVE: CONCLUSION AND RECOMMENDATION.......................................765.1 Summary of Work ............................................................................................765.2 Summary of Findings .......................................................................................775.3 Suggestions for Future Work............................................................................78

REFERENCES.................................................................................................................81

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LIST OF PUBLICATIONS ............................................................................................90

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LIST OF TABLES

Table No. Page No.

2.1 Comparison of various iris recognition algorithms 22

3.1 Statistics for CASIA-Iris-Interval 27

3.2 Truth table of XOR functions 38

4.1 Selection of separation point 46

4.2 Combination between left eye and right eye of different person 48

4.3 Combinations of between left eye and right eye of same person 69

4.4 Comparison of recognition rate 75

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LIST OF FIGURES

Figure No. Page No.

1.1 Processes involved in Biometric Recognition 2

1.2 Internal structure of a human eye 3

1.3 Complete flow diagram of the research methodology 7

3.1 Complete flow diagram of the iris recognition systrm 26

3.2 An eye image with various edge maps 30

3.3 Daugman’s rubber sheet model 32

3.4 Framework of the normalization process with constant dimension 34

3.5 An illustration of feature encoding step 37

4.1 Detection of iris-sclera and of iris-pupil boundaries 41

4.2 Detection of noise caused by eyelid and eyelash 42

4.3 Detection of iris-sclera and of iris-pupil boundaries 42

4.4 Detection of iris-sclera and iris-pupil boundaries for less ideal image 43

4.5 Normalized iris image using Cartesian to polar transformation 44

4.6 Polar image of iris and noise mask after feature extraction 45

4.7 Result of intra class variation 47

4.8 Result of inter class variation 47

4.9 Result of comparison (data1-data9) 58



4.12 Result of comparison (data28 -data36) 59


xiv


















4.31 Comparison between same eyes of a person 74

4.32 Comparisons between intra classes and inter class 74

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LIST OF ABBREVIATIONS

CASIA Institute of Automation, Chinese Academy of SciencesCFDA Complex Fisher Discriminate AnalysisFAR False accept rateFRR False reject rateGLSM Gray level co-occurrence matricesICE Iris Challenge EvaluationKFDA Kernel Fisher Discriminate AnalysisPAMI Pattern Analysis and Machine intelligencePSF Point spread functionPUM Posterior union modelQ-tables Quantization tablesROI Region-of-interest-codingSVM Support vector machine

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LIST OF SYMBOLS

xc Centre coordinate

yc Centre coordinate

R Radius

hj Peak of parabola

kj Peak of parabola

θj Angle of rotation

(r,θ) Polar coordinates

I(x,y) The image of original iris region

(x,y) Coordinate of original Cartesian form

(r,θ) Coordinates of corresponding normalized polar form

(xp, yp) Pupil coordinates

(x1, y1) Iris boundaries coordinates

(ox, oy) Iris centre

r' Distance between pupil and iris

fo Center frequency

Σ Filters bandwidth

Tb Total number of bits

Ar Angular resolution

Rr Radial resolution

Nf Number of filters

Nz Sum of zeros

Nb Sum of bits

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CHAPTER 1

INTRODUCTION

1.1 BACKGROUND

The term ‘biometric’ is a Greek word. Bio means life and metric means to measure.

The technology for measuring a person’s characteristics (both physical and

behavioural) is refers to biometric. Biometric differentiate people based on their

unique characteristics manner. Application such as passenger control in airports,

access control in restricted areas, boarder control, database access and financial

services are some of the example where the biometric technology has been applied for

more reliable identification and verification. There are different kinds of biometric

available in present world like iris recognition, face recognition, signature recognition,

retina scanning, gait recognition, palm-print verification etc. In today’s growing world

it is not safe to rely only on user id and password. It’s become harder for security

professional to detect fraud by depending on their id and password only.

Now the question is how biometric actually works. To answer this question we

have to simplify every step. The first step of biometric is either scanning particular

feature or recording signals. Then the biometric sample is transform to biometric

template by using some mathematical model. The template represents highly

discriminating feature. In order to determine the person identity the system need to

compare two templates derived from same person’s feature. Generally, these two

templates come from two modes of operation of biometric system. One is enrolment

mode another is identification mode. In enrolment mode the acquired template have to

CHAPTER ONE

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be saved in a database so that at identification mode the system can check whether the

entry is already in the database or not. Figure 1.1 shows how biometric system works.

Figure 1.1: Processes involved in Biometric Recognition

The most important aspect of biometric system is to select the feature that is

unique, stable and can be captured easily. It needs to be unique because it will be risky

if two people have same characteristics. Stable means the feature should have not

change with time. In addition, is easily captured means if it is not easy to capture then

the system will be inconvenient to the user.

Therefore, to extend and enhance the traditional system, a high security

authentication solution like iris recognition needed.

1.2 THE HUMAN IRIS

Iris is an internal organ and very well protected from outside surroundings. It is

known to be unique because of its geometric configuration. The complex structure of

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iris has high number of degree of freedom. Research proves that over a person life the

iris is essentially irreversible. During third month of gestation iris begun to form

(Kronfeld, 1962). By eight month the structure creates its remarkable pattern (Wybar,

1977). The iris diaphragm is thin and circular. In human eye it is situated between

cornea and lens. Iris is located very close to pupil which is a round aperture.

Generally, iris main job is to control the amount of light which is entering through

pupil. Iris helps pupil to adjust its size by stretching and contracting. The outside layer

of the iris is sclera. From outside it looks like iris is encircled by the white tissue of

eye. Figure 1.2 gives an idea about the internal structure of eye.

Figure 1.2: Internal structure of a human eye (Wybar, 1977)

The formation of iris pattern is random and not related to any genetic factor (Wilds,

1997). The only genetic factor of iris is its colour. The pattern of iris is epigenetic. For

that reason, each eye of same person has independent iris pattern. Even for twin its

pattern proves to be independent.

The iris is an appropriate automated and highly reliable form of personal

identification due to its texture uniqueness and immutability. As for the hardware

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implementation, the system only needs camera technology with infrared illumination

so that no physical contact is required.

1.3 IRIS RECOGNITION

Almost two decades ago professor John Daugman published a paper to IEEE

Transaction on Pattern Analysis and Machine intelligence (PAMI) presenting his first

theoretical idea about iris recognition (Daugman, 2004). Later on he got his patent for

the software (Masek, 2003). Currently many companies like Panasonic, LG-Iris, Oki,

Iridian are using his software. The algorithm developed by John Daugman remains the

basis of all significant renowned algorithm of iris recognition.

In recent years, the development and practice in the field of iris recognition has

expanded dramatically. Now it becomes a practical area of science and technology.

Currently various national identity schemes are in developing stage by using iris

recognition system. Researchers from around the world are studying about iris

recognition to make it even more accurate so that the system can be used in large

scale. At present time large part of the researchers are focusing on advance part rather

than focusing on different segmentation algorithms and texture filter. Focus is now

shift to improve accuracy for less ideal image, live tissue verification, minimizes user

cooperation during image capturing. However still one of the major research topics

understands basic science regarding iris recognition.

1.4 WHY IRIS RECOGNITION

According to (Daugman & Cathryn, 2001) the main concept of iris recognition is the

failure of a test of statistical independence. To check the randomness and uniqueness

of human iris pattern the researchers mathematically compare 2.3 million different

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pair of eye image resulting 244 independent degrees of freedom. Due to iris high

degree of freedom it is indeed a very good form of biometric identification.

1.5 PROBLEM STATEMENT

At present time large part of the researchers are focusing on advanced part. However,

lack of publicly available algorithm still a major issue when it comes to understand

basic science of iris recognition algorithm. The major research issues regarding iris

recognition are as follows

1. Dealing with non-ideal image: Currently major emphasis is on analysing

the image when acquisitions occur under less controlled condition. Less-

constrained environment generally makes the image more complex. Non-

ideal image not only refer to occlusion by eyelid or presence of specular

highlights in the image but also refer various kind of noise like blur due to

out of focus, off-angled iris, smudged iris boundaries etc. Most of the

developed algorithms for auto localization of iris in a given image fail to

localize iris due to non-ideal image. Non-ideal image also has an impact in

template matching stage. Due to non-ideal image the percentage of false

accept rate and false reject rate increases.

2. Spoofing: In present the developed iris recognition algorithms fail to

detect live tissue during image capturing. The system can be easily fooled

by high resolution image of eye. As a result spoofing may occur any time.

To prevent any kind of security breach it is essential to add extension with

developed algorithm for live tissue verification.

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1.6 OBJECTIVES

The objectives of the research are as follows

1. To develop an algorithm for personnel identification using iris localization

method.

2. To improve the identification accuracy with template matching technique.

3. To validate the performance of the algorithm for both ideal and non-ideal

eye images.

1.7 RESEARCH METHODOLOGY

This research conducted in order to achieve the objectives according to the following

procedures:

1. The research started by exploring an extensive literature review on iris

recognition.

2. Collection of data from publicly available database like the Institute of

Automation, Chinese Academy of Sciences (CASIA) database,

Multimedia University (MMU) database and UBIRIS database.

3. The process of locating the iris region in an acquired input eye image

known as iris localization is done by applying Hough transforms and

image thresholding method.

4. Verification of algorithm performance by applying algorithm on CASIA

iris interval database, MMU database and UBIRIS database.

5. Once the iris region is segmented, the next stage is to normalize this part

so as to enable the generation of the iris code and their comparison. Since

the variations in the eye like optical size of iris, position of pupil in the iris

and the orientation change from person to person, it is required to

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normalize the iris so that the representation is common to all with similar

dimension. The normalization process involves unwrapping the iris and

converting the iris into its polar equivalent which is done by applying

Daugman rubber sheet model.

6. The main goal of feature extraction is to extract the discriminating

characteristics of the iris textures and generate iris code which is done by

applying 1D Log-Gabor filter.

7. To check if the image arises from same class or from different class the

template matching is done by applying XOR functions and by calculating

percentage of zero in resulting matrix.

8. Verification of system performance on CASIA iris interval database by

calculating false rejection rate and false acceptance rate of algorithms.

9. The development tool MATLAB is used for algorithm development which

offers an excellent image processing toolbox.

Figure 1.3: Complete flow diagram of the research methodology

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1.8 SCOPE OF RESEARCH

The research work involves the development of an efficient algorithm to recognize

human iris that can be useful for individual identification. The main emphasis of the

research will be software part not the hardware part for image capturing

1.9 ORGANIZATION OF THE THESIS

The thesis comprises five chapters. The illustration of each chapter can be summarize

briefly as follows:

1. Chapter one: Introduction to iris recognition system. It consists of

background, objective, research methodology, scope of research and thesis

outline.

2. Chapter two: Introduces the literature review of the research. It also

represents the related works of this research that have been done.

3. Chapter three: Describe the details of every important stage of iris

recognition which are image pre-processing stage, feature extraction stage

and matching template stage.

4. Chapter four: Describe the experimental result of iris recognition system.

5. Chapter five: Summarizes and concludes the thesis. The contribution of

this research and suggestions of the future works.

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CHAPTER 2

LITERATURE REVIEW

2.1 INTRODUCTION

In today’s world, biometrics is one of the important parts of technology. Everyday

more than 100 trillion or 10-to-the-14th-power comparison of iris performed as a form

of identification (Daugman, 2013:vii). The Indian government already started their

vast project regarding duplicate national identities organized by the Universal

Identification Authority of India. To finish the project within three years, every day

they enrolled 1 million people in the system by using their iris pattern. The goal of this

project is to give everyone an identity. On a daily basis when the authority enrols 1

million people at the same time, they have to run cross-comparison to check for

duplication. Several countries from across the world already started similar national

identity schema.

The beginning of the research of iris as a form of identification started with the

idea of an ophthalmologist. Francis Heed Adler, an internationally renowned eye

surgeon, presented the idea in his Physiology of the Eye: “In fact, the markings of the

iris are so distinctive that it has been proposed to use photographs as a means of

identification, instead of fingerprints” (Adler, 1933:143). Survey shows, in recent year

the number of publication about iris recognition is vast compare to the number of

publication in first 15 years of proposed system (Bowyer et al., 2013).

This chapter discusses the related work on various aspect of iris recognition as

an authentic identification technique, mainly the traditional algorithm and current

focus to improve the system.

CHAPTER TWO

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