ENGLISH

Swarm Intelligence for Iris Recognition

By Zaheera Zainal Abidin

Book information

Publisher
CRC Press
Year
2021
ISBN
0367627477, 9780367627478
Language
english
Format
PDF
Filesize
6 MB (6762682 bytes)
Edition
1
Pages
136\146
Time added
2021-09-09 17:11:27

Description

Iris recognition is one of the highest accuracy techniques used in biometric systems. The accuracy of the iris recognition system is measured by False Reject Rate (FRR), which measures the authenticity of a user who is incorrectly rejected by the system due to changes in iris features (such as aging and health condition) and external factors that affect iris image, for instance, high noise rate. External factors such as technical fault, occlusion, and source of lighting that causes the image acquisition to produce distorted iris images create error, hence are incorrectly rejected by the biometric system. FRR can be reduced using wavelets and Gabor filters, cascaded classifiers, ordinal measures, multiple biometric modalities, and a selection of unique iris features. Nonetheless, in the long duration of the matching process, existing methods were unable to identify the authenticity of the user since the iris structure itself produces a template changed due to aging. In fact, the iris consists of unique features such as crypts, furrows, collarette, pigment blotches, freckles, and pupils that are distinguishable among humans. Earlier research was done by selecting unique iris features. However, these had low accuracy levels. A new way of identifying and matching the iris template using the nature-inspired algorithm is described in this book. It provides an overview of iris recognition that is based on nature-inspired environment technology. The book is useful for students from universities, polytechnics, community colleges; practitioners; and industry practitioners. Cover Title Page Copyright Page Preface Acknowledgement Table of Contents List of Figures 1. Introduction 2. Human Eye 2.1 Overview 2.2 Iris Structure 2.3 The Use of Iris for Biometric System 2.4 Summary 3. The First Phase of Iris Recognition 3.1 Overview 3.2 Enrolment Process 3.2.1 Image Acquisition and Iris Database 3.2.2 Circular Segmentation and Normalization 3.2.3 Extraction 3.3 Iris Template Storage 3.4 Comparison Process 3.4.1 Identification (Comparison for One-to-Many) 3.4.2 Verification (Comparison for One-to-One) 3.5 Challenges in the First Phase of Iris Recognition 3.5.1 Cost of Biometrics System 3.5.2 Threats and Attacks in Biometrics 3.5.3 Hardware and Software Limitations 3.5.4 Iris Distortion 3.5.5 Handling Poor Quality Data 3.6 Summary 4. The Second Phase of Iris Recognition 4.1 Overview 4.2 Short Range Iris Recognition 4.2.1 Non-Circular Segmentation 4.2.2 Artificial Intelligence Based Segmentation and Normalization 4.3 Long Range Iris Recognition 4.3.1 Iris Detection at-a-Distance (IAAD) Framework 4.4 Challenges in Second Phase of Iris Recognition 4.4.1 Pre-processing 4.4.2 Feature Extraction 4.4.3 Template Matching 4.4.4 Sensors 4.4.5 Iris Template Security 4.5 Summary 5. Swarm-Inspired Iris Recognition 5.1 Overview 5.2 Ant Colony Optimization 5.2.1 ACO Algorithm 5.2.2 ACO Pseudocode 5.2.3 Case Study: Enhanced ACO based Extraction of Iris Template 5.2.4 The Experiment Results and Findings 5.3 Particle Swarm Optimization 5.3.1 PSO Algorithm 5.3.2 Case Study: The Proposed Design and Approach Method 5.3.3 Identification Phase in Iris Recognition System 5.3.4 Hamming Distance of Intra-Class Image 5.3.5 FAR and FRR Value 5.4 Discussions 5.5 Summary 6. Conclusion References Index

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