Swarm Intelligence for Iris Recognition

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.

虹膜辨識是生物特徵系統中使用準確度最高的技術之一。虹膜辨識系統的準確度是通過偽拒絕率（FRR）來衡量的，該指標衡量了由於虹膜特徵的變化（如衰老和健康狀況）和影響虹膜圖像的外部因素（例如高噪音率）而被系統錯誤拒絕的用戶的真實性。外部因素，如技術故障、遮蔽和造成圖像獲取產生扭曲虹膜圖像的照明來源，會產生錯誤，因此被生物特徵系統錯誤拒絕。可以使用小波和Gabor濾波器、級聯分類器、序數度量、多種生物特徵模態以及選擇獨特虹膜特徵來降低FRR。然而，在長時間的匹配過程中，現有方法無法識別用戶的真實性，因為虹膜結構本身會因衰老而產生模板變化。事實上，虹膜包含了人類之間可區分的獨特特徵，如隱窩、皺紋、領圈、色素斑點、雀斑和瞳孔。早期的研究是通過選擇獨特的虹膜特徵來進行的。然而，這些方法的準確度較低。

本書描述了一種使用自然啟發算法來識別和匹配虹膜模板的新方法。它提供了基於自然啟發環境技術的虹膜辨識概述。本書對於大學、理工學院、社區學院的學生、從業人員以及行業從業人員都是有用的。

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.

Zaheera Zainal Abidin是一位專案分析師、程式設計師、培訓師和講師。自2009年以來，她一直擔任馬來西亞麻六甲理工大學（UTeM）的高級講師和研究員。她在計算機網絡領域獲得了CISCO認證（CCNA），並且是認證的物聯網專家，教授的科目包括數據通信、計算機網絡、專案管理、網絡安全和物理安全。她在書籍、研究期刊（索引和非索引）以及會議論文和研究資助提案中發表了章節。此外，她是《高級計算技術和應用期刊》（JACTA）的副主編，並審查期刊文章。她獲得了馬來西亞教育部（FRGS、PRGS和TRGS）和行業（PPRN）的研究資助。此外，她熱衷於撰寫有關計算機科學和信息安全的文章。她在澳大利亞坎培拉大學獲得了資訊技術學士學位，並加入了埃克森美孚吉隆坡區域中心擔任專案分析師。她在馬來西亞雪蘭莪州沙亞南的馬來西亞理工大學計算機和數學科學學院完成了量化科學碩士學位。2005年，她在馬來西亞吉隆坡大學（UNIKL-MIIT）擔任講師，同時在馬來西亞雪蘭莪州沙亞南的馬來西亞理工大學計算機和數學科學學院完成了計算機網絡碩士學位。2009年，她加入了馬來西亞麻六甲理工大學（UTeM），並在馬來西亞雪蘭莪州沙亞南的馬來西亞理工大學計算機和數學科學學院完成了博士學位（2016年）。她在2017年UTeM展覽中憑藉基於增強螞蟻群優化的特徵提取獲得銀獎，並在2019年UTeM展覽中憑藉使用Raspberry PI的人臉識別獲得銅獎。她的研究興趣包括物聯網、生物識別和網絡安全。此外，她曾與馬來西亞網絡安全局、馬來西亞衛生部和SigTech Solutions Malaysia進行諮詢。