"Iris recognition has been widely recognized as one of the most performing biometric system. The accuracy performance of iris recognition system is measured by FRR (False Reject Rate). FRR measures the genuine user who is incorrectly denied by the systemdue 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 to produce distorted iris images problem hence incorrectly rejected by the biometric 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. Nonetheless, in the long duration of matching process, the previous methods unable to identify the user as a genuine since the iris structure itself produce a template changed due to aging. In facts, iris 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. Therefore, a new way of identifying and matching the iris template using nature-inspired algorithm is proposed in this book. As a conclusion, this book entitled as "Swarm Intelligence for Iris Recognition" brings an overview of iris recognition that naturally based on nature-inspired environment technology and provides benefits to the reader"--
Swarm intelligence has been one of the methods of natural computing that falls under the artificial intelligence. The swarm intelligence is the winning algorithm since it searches for the genuine user effectively and tolerates with high noise in the iris template during the matching process in the iris recognition.
| Preface |
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iii | |
| Acknowledgement |
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v | |
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viii | |
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1 | (10) |
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11 | (7) |
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11 | (2) |
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13 | (3) |
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2.3 The Use of Iris for Biometric System |
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16 | (1) |
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17 | (1) |
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3 The First Phase of Iris Recognition |
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18 | (27) |
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18 | (2) |
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20 | (10) |
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3.2.1 Image Acquisition and Iris Database |
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20 | (2) |
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3.2.2 Circular Segmentation and Normalization |
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22 | (3) |
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25 | (5) |
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3.3 Iris Template Storage |
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30 | (2) |
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32 | (2) |
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3.4.1 Identification (Comparison for One-to-Many) |
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33 | (1) |
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3.4.2 Verification (Comparison for One-to-One) |
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33 | (1) |
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3.5 Challenges in the First Phase of Iris Recognition |
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34 | (10) |
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3.5.1 Cost of Biometrics System |
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36 | (1) |
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3.5.2 Threats and Attacks in Biometrics |
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37 | (1) |
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3.5.3 Hardware and Software Limitations |
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38 | (1) |
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38 | (3) |
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3.5.5 Handling Poor Quality Data |
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41 | (3) |
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44 | (1) |
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4 The Second Phase of Iris Recognition |
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45 | (21) |
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45 | (1) |
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4.2 Short Range Iris Recognition |
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46 | (10) |
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4.2.1 Non-Circular Segmentation |
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46 | (5) |
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4.2.2 Artificial Intelligence Based Segmentation and Normalization |
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51 | (5) |
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4.3 Long Range Iris Recognition |
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56 | (4) |
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4.3.1 Iris Detection at-a-Distance (IAAD) Framework |
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57 | (3) |
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4.4 Challenges in Second Phase of Iris Recognition |
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60 | (5) |
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60 | (1) |
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61 | (1) |
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62 | (1) |
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63 | (1) |
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4.4.5 Iris Template Security |
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64 | (1) |
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65 | (1) |
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5 Swarm-Inspired Iris Recognition |
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66 | (51) |
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66 | (7) |
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5.2 Ant Colony Optimization |
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73 | (24) |
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77 | (5) |
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82 | (1) |
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5.2.3 Case Study: Enhanced ACO based Extraction of Iris Template |
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83 | (10) |
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5.2.4 The Experiment Results and Findings |
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93 | (4) |
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5.3 Particle Swarm Optimization |
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97 | (17) |
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100 | (4) |
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5.3.2 Case Study: The Proposed Design and Approach Method |
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104 | (4) |
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5.3.3 Identification Phase in Iris Recognition System |
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108 | (4) |
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5.3.4 Hamming Distance of Intra-Class Image |
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112 | (1) |
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113 | (1) |
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114 | (2) |
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116 | (1) |
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117 | (2) |
| References |
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119 | (14) |
| Index |
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133 | |
Zaheera Zainal Abidin was a project analyst, programmer, trainer and lecturer. She has been Senior Lecturer and Researcher at Universiti Teknikal Malaysia Melaka (UTeM) since 2009. She is CISCO certified (CCNA) in the computer networking field and certified Internet-of-Things specialist, and teaches subjects such as data communication, computer networks, project management, network security and physical security. She has published chapters in books, research journals (indexed and non-indexed) and proceedings and research grant proposals. Also, she is associate editor-in-chief for Journal of Advanced Computing Technology and Application (JACTA) and reviews journal articles. She has been awarded research grants from Ministry of Education Malaysia (FRGS, PRGS and TRGS) and industry (PPRN). Moreover, she loves to write about computer science and information security. She received Bachelor of Information Technology from University of Canberra, Australia and joined ExxonMobil Kuala Lumpur Regional Center as a Project Analyst. She completed her MSc. in Quantitative Sciences at the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia. In 2005, she served as a lecturer at Universiti Kuala Lumpur (UNIKL-MIIT) and as a program coordinator while completing her MSc. in Computer Networking also at the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia. In 2009, she joined Universiti Teknikal Malaysia Melaka (UTeM) and completed her PhD (2016) in IT and Quantitative Sciences from the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia. She won a silver award at 2017 UTeM Exhibition on Feature Extraction based on Enhanced AntColonyOptimization for Iris Identification and a bronze award for Face Recognition using Raspberry PI at 2019 UTeM Exhibition. Research interests include Internet-of-Things, biometrics and network security. Also, she did consultations with Cyber Security Malaysia, Ministry of Health Malaysia and SigTech Solutions Malaysia.
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