UNIVERSITI TEKNOLOGI MARA FURROW AND CRYPT DETECTION USING MODIFIED ANT COLONY OPTIMIZATION FOR IRIS RECOGNITION ZAHEERA ZAINAL ABIDIN.

Transcription

1 UNIVERSITI TEKNOLOGI MARA FURROW AND CRYPT DETECTION USING MODIFIED ANT COLONY OPTIMIZATION FOR IRIS RECOGNITION ZAHEERA ZAINAL ABIDIN PhD January 2016

2 UNIVERSITI TEKNOLOGI MARA FURROW AND CRYPT DETECTION USING MODIFIED ANT COLONY OPTIMIZATION FOR IRIS RECOGNITION ZAHEERA ZAINAL ABIDIN Thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy Faculty of Computer and Mathematical Sciences January 2016 i

3 CONFIRMATION BY PANEL OF EXAMINERS I certify that a panel of examiners has met on 3 rd November 2015 to conduct the final examination of Zaheera binti Zainal Abidin on her Doctor of Philosophy thesis entitled Furrow and Crypt Detection using Modified Ant Colony Optimization for Iris Recognition in accordance with Universiti Teknologi MARA Act 1976 (Akta 173). The Panel of Examiners recommends that the student be awarded the relevant degree. The panel of Examiners was as follows: Daud Mohamad, PhD Professor Faculty of Computer & Mathematical Sciences Universiti Teknologi MARA (Chairman) Anil K. Jain, PhD Professor Department of Computer Science & Engineering Michigan State University, East Lansing, Michigan, USA (External Examiner - International) Abd Rahman bin Ramli, PhD Associate Professor Faculty of Engineering Universiti Putra Malaysia (External Examiner - National) Noor Elaiza binti Abd. Khalid, Phd Senior Lecturer Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA, Malaysia (Internal Examiner) SITI HALIJJAH SHARIFF, PhD Associate Professor Dean Institute Graduate Studies Universiti Teknologi MARA Date: 26 January 2016 ii

4 AUTHOR'S DECLARATION I declare that the work in this thesis was carried out in accordance with the regulation of Universiti Teknologi MARA. It is original and is the result of my own work, unless otherwise indicated or acknowledged as referenced work. This thesis has not been submitted to any other academic institution or non-academic institution for any other degree or qualification. I, hereby, acknowledge that I have been supplied with the Academic Rules and Regulations for Post Graduate, Universiti Teknologi MARA, regulating the conduct of my study and research. Name of Student : Zaheera binti Zainal Abidin Student's ID No. : Programme : PhD in Science Faculty : Faculty of Computer and Mathematical Sciences Thesis Title : Furrow and Crypt Detection using Modified Ant Colony Optimization for Iris Recognition Signature of Student :. Date : January 2016 iii

5 ABSTRACT Iris recognition has been widely recognized as one of the most performing biometric system. The accuracy performance of iris recognition system is measured by FAR (False Accept Rate) and FRR (False Reject Rate). FRR measures the genuine that is incorrectly denied by the system due to the changes in iris features (such as aging and health condition) and external factors that affected the iris image to be high in noise rate. The external factors such as technical fault, occlusion, and source of lighting caused the image acquisition which produce distorted iris images problem hence incorrectly rejected by the system. The current way of reducing FRR are wavelets and Gabor filters, cascaded classifiers, ordinal measure, multiple biometric modality and selection of unique iris features. Iris structure consists of unique features such as crypts, furrows, collarette, pigment blotches, freckles and pupil that are distinguishable among human. Previous research has been done in selecting the unique iris features however it shows low accuracy performance. As a solution, to improve the accuracy performance, this research proposes a new approach called as Modified Ant Colony Optimization that uses ant colony algorithm which search for crypts and radial furrow. The method consists of two tasks in obtaining the crypt and radial furrow features from the iris texture. The first task is the artificial ants that scan the pixel values according to the range of selected crypt or radial furrow. Then, the scanned pixels value is searched based on degree of angle (0 o, 45 o, 90 o and 135 o ). The second task produces the confusion matrix and the blob of iris feature image is marked and indexed before stored into the database. In order to evaluate the performance of the proposed approach, FAR and FRR are measured with Chinese Academy of Sciences' Institute of Automation (CASIA) database for high quality images and Noisy Visible Wavelength Iris Image Databases (UBIRIS) database for noisy iris. By using CASIA version 3 image databases, the crypt feature shows that the result of FRR is 18.05% and radial furrow gives 81.5% when FAR at 0.1%. For UBIRIS version 1 database, the crypt feature indicates that the value FRR is 46.93% meanwhile the radial furrow shows the values of FRR 33.87% when FAR at 0.1%. To evaluate Modified Ant Colony Optimization, the genuine acceptance value (GAR) is measured to recognize iris features detection in low quality image environment. The experiment finding indicates that by using the Modified Ant Colony Optimization, radial furrow is able to be detected in distorted iris images with 84.62% since its own characteristics is obviously revealed. Moreover, the intersaction between FAR and FRR produces the Equal Error Rate (EER) with 0.21%, which indicated that equal error rate is lower than the previous standard value, which is 0.3%. Therefore, the advantages of using Modified Ant Colony Optimization are it has the capability to adapt with unique iris features in robust manner and use small amount of information in unique micro-characteristics of iris features to determine the user. The outcome of this new approach is to reduce the EER rates since lower EER rates indicates better accuracy performance. As a conclusion, the contribution of Modified Ant Colony Optimization extraction approach brings an innovation at the extraction process in the biometric technology and provides benefits to the communities. iv

6 ACKNOWLEDGMENT First and foremost, I would like to thank Allah the Almighty, for his guidance, ideas and comfortable environment. I would like to extend my appreciation to the Universiti Teknikal Malaysia Melaka and Ministry of Education Malaysia for their generosity for awarding me the scholarship during this study. Thank you to my supervisor, Professor Hj. Dr. Mazani Manaf, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, and co-supervisors, Assoc Prof. Dr. Abdul Samad Shibghatullah, Department of Computer Systems and Communications, Faculty of Information and Communication Technology (FTMK), Universiti Teknikal Malaysia Melaka (UTeM). Without their sincere guidance, this work would not have been possible. A special thank and higly appreciation to Professor Dr. Anil K. Jain as an external examiner (international) with good comments, Assoc. Prof. Dr. Abdul Rahman Ramli as an external examiner (national), Dr. Noor Elaiza Abd Khalid (internal examiner), Professor Zhenan Sun and Associate Professor Dr. Sarat C. Dass for giving fruitful comments about my work. Thank you to Libor Masek who shares the matlab codes. An appreciation to Prof. Dr. Rabiah Ahmad and Associate Professor Dr. Choo Yun Huoy who have helped and supports in completing my study. A special thank to Syarulnaziah Anawar, Zakiah Ayop, Nor Azman Mat Ariff, Nurul Akmal Hashim, Dr. Zuraida Abal Abas, Ahmad Fadzli Nizam Abdul Rahman, Hidayah Rahmalan, and Mohd Zaki Mas ud for their constructive discussions and helps with the analysis and in thesis writing during the course of this study. Last but not least, from the bottom of my heart a gratitute to my family for their love and caring. Special thanks to my husband, Khairul Anwar Ibrahim, for his encouragements, my eternal love to all my children, Akmal al Husainy, Umairah al Husna, and Zakeeyah al Husna, for their patience and understanding. Finally, I would like to thank my beloved parents who have been the pillar of strength in all my endeavors. I am always deeply indebted to them for all their endless love and prayers that they have given me. Thank you to the individual(s) who providing me the inspiration to embark on my study. v

7 TABLE OF CONTENTS Page CONFIRMATION BY PANEL OF EXAMINERS AUTHOR'S DECLARATION ABSTRACT ACKNOWLEDGMENT TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS ii iii iv v vi xi xiii xix CHAPTER ONE: INTRODUCTION Overview of Iris Recognition Background of the Research Problem Statement Research Aim Research Scope Research Significances Contributions of the Research Thesis Organization 11 CHAPTER TWO: LITERATURE REVIEW Introduction Human Iris Anatomy Iris Distortion and Occlusion Problem 18 vi

8 2.4 The Iris Recognition System Enrollment Process Comparison Process The Existing Methods of Iris Features Detection Wavelets and Log Gabor Filters Cascaded Classifiers Ordinal Measures Multiscale Feature Selection The Theoretical Framework of Iris Recognition The Basic Conceptual Framework of Iris Recognition The Conceptual Framework of Iris Recognition Swarm Intelligence Feature Extraction The Propose Approach (modified ant colony optimization) of Feature Extraction Summary 60 CHAPTER THREE: RESEARCH METHODOLOGY Introduction Research Framework Proposed Research Framework Phase 1: Preliminary Study Phase 2: Data Collection Phase 3: Proposed Design and Prototype Construction Phase 4: Results and Analysis Phase 5: Performance Analysis Software and Hardware Requirements Summary 91 vii

9 CHAPTER FOUR: PRE-PROCESSING ANALYSIS Introduction The Experiment Environment Thresholding process Iris Segmentation Process Iris Normalization Process Experiment Results Results of Iris Segmentation Results of Iris Normalization Experiment Findings Summary 109 CHAPTER FIVE: MODIFIED ANT COLONY OPTIMIZATION FEATURE EXTRACTION Introduction Experiment and Implementation Swarm Intelligence Extraction Experiment Setup with WEKA and Rapid Miner Modified Ant Colony Optimization Approach Experiment Results Swarm Intelligence Extraction Results on WEKA and Rapid Miner Modified Ant Colony Optimization Experiment Findings The Evaluation of Current Extraction, ACO, PSO and Modified Ant Colony Optimization Summary 153 viii

10 CHAPTER SIX: IMAGE BASED MATCHING USING CBIR Introduction Experiment Environment Experiment Results Ant Feature Index Confusion Matrix Accuracy Performance Evaluation and Validation for Iris Image Matching Accuracy Performance Evaluation and Validation for Iris Image Matching Iris Database Used for Validation Existing Approach Used for Validation Experiment Findings Summary 173 CHAPTER SEVEN: THE NEW APPROACH OF IRIS RECOGNITION (Modified Ant Colony Optimization + MATCHING) AND EXPERIMENT FINDINGS Introduction Experiment Environment Experiment Results ACO and Modified Ant Colony Optimization Pattern of Ant Movement Threshold Value Setup The ROC Curve Experiment Findings High Quality Iris Images Low Quality Iris Images (Noisy Iris) Discussion Modified Ant Colony Optimization Implementation Implication of Modified Ant Colony Optimization and future prospects 189 ix

11 7.6 Summary 189 CHAPTER EIGHT: CONCLUSION Conclusion Recommendation 195 REFERENCES 196 AUTHOR S PROFILE 212 x

12 LIST OF TABLES Table Title Page Table 2.1 Statistics of CASIA 22 Table 2.2 The Critical Review Evaluation with RQ1, RQ2 and RQ3 Map 55 Table 2.3 Comparison of Data Mining Tools 56 Table 3.1 Research Methodology Mapping with RO1, RO2 and RO3 91 Table 4.1 PSNR Values of Canny and Sobel using CASIA database 98 Table 4.2 HT and IDO Techniques for Iris Segmentation Process 102 Table 4.3 Open Iris Database Accuracy Performance 103 Table 4.4 Iris Normalization 104 Table 4.5 Iris Normalization Process 104 Table 5.1 The Parameter Settings for PSO, ACO and Modified Ant Colony Optimization 117 Table 5.2 The Sample of Scoresheet for Precision and Recall Calculation 121 Table 5.3 Texture Extraction Results 122 Table 5.4 Bio-inspired Feature Selection in Texture Analysis Extraction 127 Table 5.5 Modified Ant Colony Optimization Pheromone Table based on numberof iteration and index for CASIA 133 Table 5.6 Modified Ant Colony Optimization Pheromone Table based on number of iteration and index for UBIRIS 134 xi

13 Table 5.7 Modified Ant Colony Optimization Scoresheet for Precision and Recall Calculation 135 Table 5.8 Summary of Modified Ant Colony Optimization Precision 139 Table 5.9 Table 5.10 Experiments Results of PSO, ACO and Modified Ant Colony Optimization in Extraction 141 The Characteristics in PSO, ACO and Modified Ant Colony Optimization 147 Table 6.1 Iris Database Classification 156 Table 6.2 Iris Matching in Testing Set 164 Table 6.3 Results of Sample Data for Testing in Matching Process 167 Table 6.4 Comparison of ACO and Modified Ant Colony Optimization 170 Table 7.1 Score sheet results of precision using Modified Ant Colony Optimization 178 xii

14 LIST OF FIGURES Figures Title Page Figure 1.1 Taxonomy of Contribution Map 11 Figure 1.2 Research Schematic Diagram 13 Figure 2.1 Iris Structure 16 Figure 2.2 Crypt 16 Figure 2.3 Furrow 17 Figure 2.4 Sample of pigment melanin 17 Figure 2.5 Sample of rare blotches in blob of iris features 17 Figure 2.6 Daugman s Approach of Iris Recognition 19 Figure 2.7 Iris Biometric System Phase 20 Figure 2.8 The customized or self-developed iris camera CASIA 22 Figure 2.9 Examples of iris images in CASIA-Iris-Interval 22 Figure 2.10 UBIRIS version 1 24 Figure 2.11 Circular Segmentation 25 Figure 2.12 Non Circular Segmentation 25 Figure 2.13 Partial Segmentation 26 Figure 2.14 Real and Imaginary Parts 31 Figure 2.15 Energy Histogram in DCT sub-band 31 Figure 2.16 Compression in Iris 32 Figure 2.17 Compression and Decomposition 32 Figure 2.18 Iris texture pattern 34 Figure 2.19 Interrelation of FAR, FRR, and EER Accuracy Performance 35 xiii

15 Figure 2.20 Problems in Iris Recognition 36 Figure 2.21 Pupilary zone contraction due to light source 37 Figure 2.22 Ordinal measures 39 Figure 2.23 Segmentation and Normalization using Multiscale 40 Figure 2.24 Feature Extraction using Multiscale 41 Figure 2.25 Feature Selection Method in Iris Recognition 42 Figure 2.26 System framework with manual inspection 42 Figure 2.27 The Theoretical Framework of Iris Recognition 43 Figure 2.28 The Basic Conceptual Framework of Iris Recognition 44 Figure 2.29 The Conceptual Framework of Iris Recognition 45 Figure 2.30 The Principle of ACO 50 Figure 2.31 The Modified Ant Colony Optimization Model 51 Figure 2.32 Formula of Precision, Recall and Accuracy 59 Figure 3.1 The Structure of Chapter 3 62 Figure 3.2 Research Framework 63 Figure 3.3 The Proposed Research Framework 65 Figure 3.4 Preliminary Study Phase 66 Figure 3.5 Data Collection Phase 66 Figure 3.6 Pre-processing Phase 67 Figure 3.7 Iris Normalization 69 Figure 3.8 Extraction Phase 71 Figure 3.9 The Overview of Modified Ant Colony Optimization Approach 72 Figure 3.10 The Procedure of Modified Ant Colony Optimization Approach 73 Figure 3.11 Region of interests for radial furrow, crypt, eyelids and eyelashes 75 Figure 3.12 Ant Movements Based on Degrees of Angle 76 xiv

16 Figure 3.13 Ant Movements Based on Degrees of Angle 77 Figure 3.14 The Ant Stigmergy 80 Figure 3.15 The Pseudocode of Co-occurrence Matrix 82 Figure 3.16 The Comparison Process in Iris Recognition 83 Figure 3.17 The Image based Matching in Iris Recognition 85 Figure 3.18 The Identification and Verification Mode in Matching Process 86 Figure 3.19 Results and Analysis 87 Figure 3.20 Performance Analysis 89 Figure 4.1 The experimental framework of pre-processing techniques 93 Figure 4.2 Iris Pre-conditioning Phase 94 Figure 4.3 Pseudocode of IDO 97 Figure 4.4 Edge Detection Techniques 98 Figure 4.5 The application of Canny edge detection to iris image 99 Figure 4.6 Pseudocode of Hough Transform 100 Figure 4.7 The iris normalization for comparison process 105 Figure 4.8 Comparison of GAR based FRR 105 Figure 4.9 Comparison of GAR based RE-rate 106 Figure 4.10 Average of circle pupil and circle iris comparison in CASIA 106 Figure 4.11 FAR versus FRR 107 Figure 4.12 FAR versus FRR Threshold 108 Figure 5.1 Experiment Planning for Iris Extraction using PSO and ACO 112 Figure 5.2 Experiment Planning for Iris Extraction using MACO 112 Figure 5.3 The General Experimental Configuration 116 Figure 5.4 The Flowchart of Extraction Phase using MACO 118 Figure 5.5 The Modified Ant Colony Optimization Approach Procedure 120 xv

17 Figure 5.6 CASIA Precision based on Texture Analysis Extraction 123 Figure 5.7 UBIRIS Precision of Texture Analysis Extraction 123 Figure 5.8 Accuracy of Texture Analysis Approach in CASIA 124 Figure 5.9 Accuracy of Texture Analysis Approach in UBIRIS 124 Figure 5.10 Accuracy of iris texture extraction for CASIA.V3 125 Figure 5.11 Accuracy of iris texture extraction for UBIRIS.V1 126 Figure 5.12 Precision of ACO in CASIA 128 Figure 5.13 Precision of ACO in UBIRIS 128 Figure 5.14 Precision of PSO in CASIA 128 Figure 5.15 Precision of PSO in UBIRIS 128 Figure 5.16 Accuracy of ACO in CASIA 129 Figure 5.17 Accuracy of ACO in UBIRIS 129 Figure 5.18 Accuracy of PSO in CASIA 129 Figure 5.19 Accuracy of PSO in UBIRIS 129 Figure 5.20 Load Image Interface 131 Figure 5.21 Segment and Normalize Interface 131 Figure 5.22 Modified Ant Colony Optimization Extraction Interface 131 Figure 5.23 Modified Ant Colony Optimization Matching Interface Match 131 Figure 5.24 Modified Ant Colony Optimization Matching Interface Not Match 131 Figure 5.25 Precision based on Number of Ant Iteration in CASIA (Furrow) 136 Figure 5.26 Precision based on Number of Ant Iteration in UBIRIS (Furrow) 136 Figure 5.27 Precision based on Number of Ant Iteration in CASIA.v3 (Crypt) 137 Figure 5.28 Precision based on Number of Ant Iteration in UBIRIS.v1 (Crypt) 137 Figure 5.29 Ant Feature Marking of Crypts (CASIA) 138 Figure 5.30 Ant Feature Marking of Radial Furrow (CASIA) 138 xvi

18 Figure 5.31 Ant Feature Index 139 Figure 5.32 XAMPP 142 Figure 5.33 The Connection Linker 142 Figure 5.34 The MySQL 143 Figure 5.35 Ant Feature Index of Crypts (CASIA) Marking 143 Figure 5.36 Ant Feature Index of Furrow (CASIA) Marking 143 Figure 5.37 Indexed Iris Feature Retrieval Database 144 Figure 5.38 Confusion Matrix of Crypt using the MACO in CASIA 144 Figure 5.39 Confusion Matrix of Radial Furrow using the MACO in CASIA 145 Figure 5.40 WEKA sceen captured for MACO for Crypt in UBIRIS 145 Figure 5.41 WEKA sceen captured Results for MACO for Crypt in UBIRIS 146 Figure 5.42 Accuracy Performance of Extraction (UBIRIS.V1) 148 Figure 5.43 Accuracy Performance of Extraction (CASIA.V3) 149 Figure 5.44 Accuracy Performance of Bio-inspired Extraction 150 Figure 5.45 Accuracy Performance of MACO Extraction 151 Figure 5.46 A Comparison of Crypt based on PSO, ACO and MACO 152 Figure 5.47 A Comparison of Furrow based on PSO, ACO and MACO 153 Figure 6.1 The Process of Image Matching using CBIR 155 Figure 6.2 Flowchart of Matching Phase 158 Figure 6.3 IrisCodes using Libor Masek for User A 159 Figure 6.4 Iris codes using Libor Masek for User B 159 Figure 6.5 Modified Ant Colony Optimization for User A 160 Figure 6.6 Modified Ant Colony Optimization for User B 160 Figure 6.7 Ant Feature Index of Crypts (CASIA) Ant Crypt Index 161 Figure 6.8 Ant Feature Index of Radial Furrow (CASIA) Furrow Index 161 xvii

19 Figure 6.9 Ant Feature Marking based on in MACO 161 Figure 6.10 Ant Feature Indexed based on Hamming Distance 162 Figure 6.11 The Matching Process using Datasets 163 Figure 6.12 The Confusion Matrix 165 Figure 6.13 Accuracy of Ant-CBIR-RF-CASIA 169 Figure 6.14 Accuracy of Ant-CBIR-RF-UBIRIS 169 Figure 6.15 Accuracy of Ant-CBIR-Crypt-CASIA 169 Figure 6.16 Accuracy of Ant-CBIR-Crypt-UBIRIS 169 Figure 6.17 Summary of Crypt and Furrow using MACO 171 Figure 6.18 Crypt and Radial Furrow Evaluation of GAR before and after the Matching Process using the Modified Ant Colony Optimization and ACO 172 Figure 7.1 The Schematic Diagram of Chapter Figure 7.2 The New Approach of Iris Recognition 176 Figure 7.3 Genuine Score Matrix 177 Figure 7.4 FAR versus FAR for High Quality Iris (CASIA.v3) 181 Figure 7.5 FAR versus FAR for Noisy Iris (UBIRIS.v1) 182 Figure 7.6 ROC Curve of Iris Features based on Traditional, ACO and Modified Ant Colony Optimization Approach 186 Figure 8.1 The Objectives and Contributions Mapping 194 xviii

20 LIST OF ABBREVIATIONS Abbreviations RP1 Research Problem 1 RP2 Research Problem 2 RP3 Research Problem 3 RQ1 Research Question 1 RQ2 Research Question 2 RQ3 Research Question 3 RO1 Research Objective 1 RO2 Research Objective 2 RO3 Research Objective 3 RC1 Research Contribution 1 RC2 Research Contribution 2 RC3 Research Contribution 3 FAR FRR EER HD ACO MACO PSO CBIR Threshold FMR FNMR False Acceptance Rate False Rejected Rate Equal Error Rate Hamming Distance Ant Colony Optimization Modified Ant Colony Optimimzation Particle Swarm Optimization Content Based Image Retrieval Filter or Cut off Value False Match rate False Non-Match Rate xix

21 Enrollment Create and Store a Biometric Enrollment Data Record with Biometric Policy of Enrollment Blob of Iris Features The unique micro-characteristics inside the iris features such as crypt, furrow, collarette and pigment melanin. xx

22 CHAPTER ONE INTRODUCTION 1.1 OVERVIEW OF IRIS RECOGNITION Biometrics has been used worldwide as a reliable source of identification system for many applications such as restricted area access control, database access, computer login, building entry, airport security, forensic application systems and automatic teller machine (ATM). Compared to existing identification system (i.e.: smartcard and RFID), biometrics offers higher accuracy, security, efficiency, availability, uniqueness and superior performance. In most application, biometric recognition system scan a person s body parts, extract unique features and stored them in a secured database as biometric template. Then, later, when the system is invoked again by a user (e.g. a user scans his/her body parts to gain access), the system compares the database with the existing biometric template and provides indication whether the scanned images matches any of the existing iris template. If it matches, then the system allows the user to gain access, else, the system will deny access. In biometrics, various modalities such as facial shape, fingerprint, handwriting, and iris have been used for human identification and access control. Iris recognition stands out as a promising method for obtaining automated, secure, reliable, fast and high in accuracy for user identification which typically achieve 99% accuracy rate with equal error rates of less than 1% [1]. Iris recognition is an autonomous system that uses complex mathematical pattern recognition, image processing and machine learning techniques for measuring the iris [2]. Inside the human iris, there are many unique features such as crypts, radial furrows, concentric furrows, collarette, freckles, pupil and pigment blotches which distinguish the genuine characteristics of a person, thus making it suitable for recognition purposes. However, the demand for higher accuracy and high speed recognition in biometric system leads to continuous proposals of new iris recognition 1

23 method since there are still concers on the arising number of impostor situation being reported from the existing biometric system [3]. In biometric recognition process, impostor or incorrectly recognized users can be categorized into two types. The first type (Type I) of impostor is when the biometric system rejects the genuine user who wants to access the system. In most applications, a genuine user is defined as the person who is officially allowed by the system owner to gain access to a certain secured system and have his/her own biometric template captured and stored. Meanwhile, the second type (Type II) of impostor is someone who tries to penetrate the biometric systems and pretends to be the original person. In fact, this is a situation where the person does not officially allowed to gain access to a system and does not have any biometric template captured. However, in either situation, the reason why the system behaves in such condition is because when the system detects what is commonly known as iris distortion and thus, failure to match the scanned and stored biometrics identity. In the context of iris recognition system, iris distortion means that the iris characteristics captured by the system are detected to be significantly different and cannot be matched for similarity from any of the original iris template registered in the database. Section 1.2 shall deliberate in detail the cause of the iris distortion problem in iris recognition. 1.2 BACKGROUND OF THE RESEARCH From previous works, the cause of distortion to the captured iris images can be categorized into two: i) dynamic nature of iris characteristics, ii) occlusion. The first category is defined as a situation where the iris itself changes due to human s biological factors, such as aging, growth, emotion, diet, health problems and eye surgery. In fact, the colour of the iris texture may also change due to inheritance and epigenetic diversity from different races. The constantly changing iris texture creates difficulties at the comparison phase to determine either the captured iris data are genuine or not. Previous studies shows that failure was detected in 21% of intra-class comparisons cases, taken at both three and six months intervals [4]. The second category of distorted iris source simply means that the iris images could not be captured accurately due to some physical obstructions or occlusions, 2

24 such as eyelids and eyelashes [5], as well as the presence of contact lenses or spectacles. The occlusions affect some of the important and vital iris features which are unable to be captured, thus contribute to the increased error rate. Therefore, many researchers have done studies to overcome the problem of distorted iris features and occlusion based on elimination of the unwanted noise [6], [7], enhance the noise level [8], white noise application and removal [9], non-circular segmentation process [10] [13] and feature selection [14]. However, most of the research outcome indicates that existing solutions, to a certain extent, still incapable to reduce or eliminate noisy iris problems. Among all proposed solution, in order to solve this problem, the recommendation is to use only a certain unique part of the iris structure which remains unchanged for iris recognition. The unique part of the iris texture consists of crypts, furrows, collarette, pupil, freckles and blotches [2]. Subsequently, some studies found that the micro characteristics in iris features have been mostly stable for recognition [15], [16]. Nonetheless, the unique iris feature sustained only for a certain period of time, stated in [17], [18] and only up to six years, as stated in [19]. Studies on selecting the iris texture from its original structure have been gaining attention from some researchers [20] [26]. Hence, feature selection is vital for choosing a subset features of available unique features by eliminating unnecessary features since the information of iris features obtained can be tremendously huge and consequently consumes a lot of computational resources [14]. In fact, in feature selection, it is observed that the existing method lacks of natural computational element in the iris recognition. Therefore, the unique iris feature selected is based on the best features points from the entire iris texture which is required in learning the changes or instability in iris texture intelligently. The natural computational algorithms consist of artificial neural networks, artificial immune systems, evolutionary algorithms, and swarm intelligence. In swarm intelligence, the particle swarm optimization (PSO), genetic algorithms (GA), and ant colony optimization (ACO) are the most used natureinspired algorithms to solve optimization problems. Swarm algorithm is chosen based on winning their winninf criteria. Two of the most prominent criteria which makes the preferred algorithms are that these algorithms are able to search the elements 3