Iris and Periocular Recognition using Deep Learning
- 1st Edition - June 12, 2024
- Imprint: Academic Press
- Author: Ajay Kumar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 7 3 1 8 - 6
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 7 3 1 9 - 3
Iris and Periocular Recognition using Deep Learning systematically explains the fundamental and most advanced techniques for ocular imprint-based human identification, with many a… Read more
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Request a sales quoteIris and Periocular Recognition using Deep Learning systematically explains the fundamental and most advanced techniques for ocular imprint-based human identification, with many applications in sectors such as healthcare, online education, e-business, metaverse, and entertainment. This is the first-ever book devoted to iris recognition that details cutting-edge techniques using deep neural networks. This book systematically introduces such algorithmic details with attractive illustrations, examples, experimental comparisons, and security analysis. It answers many fundamental questions about the most effective iris and periocular recognition techniques.
- Provides insightful algorithmic details into highly efficient and precise iris recognition using deep neural networks
- Unveils a collection of previously unpublished results and in-depth explanations of advanced ocular recognition algorithms
- Presents iris recognition algorithms specifically designed to bolster metaverse security, featuring specialized techniques for iris detection, segmentation, and matching
- Offers illustrative examples and comparative analysis, establishing reliability and confidence in deep learning-based methods over widely used conventional methods
- Provides access to the original codes and databases
Researchers, graduate students, and professional developers in Biometrics, Artificial Intelligence, Data Security Professionals in law enforcement departments, defense and related industrial sectors; system developers and Integrators for various information security related solutions
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- 1. An insight into trends on advances in iris and periocular recognition
- Abstract
- 1.1 Ocular patterns and biometrics
- 1.2 Iris segmentation
- 1.3 Iris recognition
- 1.4 Iris recognition using mobile phones
- 1.5 Periocular recognition and fusion with iris
- 1.6 Advancing iris recognition with periocular and multispectral images
- 2. Unlocking the full potential of Iris recognition with deep learning
- Abstract
- 2.1 Limitations with classical iris recognition techniques and challenges
- 2.2 Network architecture to generate optimized iris templates
- 2.3 Extended triplet loss function
- 2.4 Fine tuning and feature fusion
- 2.5 Feature encoding and matching
- 2.6 Experiments and results
- 2.7 Chapter summary
- 3. A unified framework for accurate detection, segmentation, and recognition of irises
- Abstract
- 3.1 Introduction to the framework
- 3.2 Network architecture
- 3.3 Experiments and results
- 3.4 Conclusions and further enhancements
- 4. Enhancing iris recognition accuracy through dilated residual features
- Abstract
- 4.1 Introduction
- 4.2 Residual feature learning and dilated kernels for iris recognition
- 4.3 Experiments and results
- 4.4 Discussion
- 4.5 Chapter summary and further enhancements
- Chapter 5. Iris recognition with deep learning across spectrums
- Abstract
- 5.1 Introduction
- 5.2 Cross-spectral iris recognition
- 5.3 Experiments and results
- 5.4 Comparisons with other CNN models and hashing methods
- 5.5 Chapter summary and further enhancements
- 6. Semantics-assisted convolutional neural network for accurate periocular recognition
- Abstract
- 6.1 Introduction
- 6.2 Methodology
- 6.3 Matching periocular images using SCNN
- 6.4 Experiments and results
- 6.5 Conclusions and further enhancements
- 7. Deep neural network with focused attention on critical periocular regions
- Abstract
- 7.1 Introduction
- 7.2 Methodology
- 7.3 Visual attention analysis
- 7.4 Network training analysis
- 7.5 Experiments and results
- 7.6 Conclusions and further enhancements
- 8. Dynamic iris recognition through multifeature collaboration
- Abstract
- 8.1 Introduction
- 8.2 Dynamic iris recognition framework
- 8.3 Experiments and results
- 8.4 Comparative analysis of static and dynamic fusion
- 8.5 Ablation Study and Discussion
- 8.6 Conclusions and further enhancements
- 9. Position-specific convolutional neural network to accurately match iris and periocular images
- Abstract
- 9.1 Introduction to new network design
- 9.2 Position-specific convolutional neural network
- 9.3 3D feature representation
- 9.4 Experiments and results
- 9.5 Conclusions and further evaluation
- 10. Biometrics security in the metaverse using egocentric iris recognition
- Abstract
- 10.1 Continuous biometric authentication in the metaverse
- 10.2 Iris segmentation and normalization
- 10.3 Egocentric iris recognition framework
- 10.4 Adaptive egocentric iris recognition
- 10.5 Database development and organization
- 10.6 Experiments and results
- 10.7 Summary and further work
- 11. Inference and future pathways: reflections and exploration of new horizons
- Abstract
- 11.1 Advancements and challenges
- 11.2 Factors influencing the performance of iris recognition algorithms
- 11.3 Limitations with other published algorithms
- 11.4 Further exploration and enhancements
- Bibliography
- Index
- Edition: 1
- Published: June 12, 2024
- No. of pages (Paperback): 308
- No. of pages (eBook): 350
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780443273186
- eBook ISBN: 9780443273193
AK
Ajay Kumar
Ajay Kumar is a Professor in the Department of Computing at The Hong Kong Polytechnic University, Hong Kong. Prior to this, he served as an Assistant Professor in the Department of Electrical Engineering at IIT Delhi from 2005 to 2007. Dr. Kumar serves on the Editorial Board of IEEE Transactions on Pattern Analysis and Machine Intelligence. He is a Fellow of IEEE and IAPR and has served the IEEE Biometrics Council as its President from 2021 to 2022. His research interests include biometrics with an emphasis on iris, hand, and knuckle biometrics.
Affiliations and expertise
Department of Computing, The Hong Kong Polytechnic University Hong Kong, Kowloon, Hong KongRead Iris and Periocular Recognition using Deep Learning on ScienceDirect