This book 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 Unveils a collection of previously unpublished results and in-depth explanations of advanced ocular recognition algorithms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
1. Iris and Periocular Recognition using Deep Learning - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page vi 7. Preface - p. Page vi 8. List of Illustrations - p. Page x 9. List of Tables - p. Page xi 10. 1 : An insight into trends on advances in iris and periocular recognition - p. Page xi 11. 2 : Unlocking the full potential of Iris recognition with deep learning - p. Page 32 12. 3 : A unified framework for accurate detection, segmentation, and recognition of irises - p. Page 60 13. 4 : Enhancing iris recognition accuracy through dilated residual features - p. Page 75 14. Chapter 5 : Iris recognition with deep learning across spectrums - p. Page 95 15. 6 : Semantics-assisted convolutional neural network for accurate periocular recognition - p. Page 122 16. 7 : Deep neural network with focused attention on critical periocular regions - p. Page 144 17. 8 : Dynamic iris recognition through multifeature collaboration - p. Page 169 18. 9 : Position-specific convolutional neural network to accurately match iris and periocular images - p. Page 193 19. 10 : Biometrics security in the metaverse using egocentric iris recognition - p. Page 210 20. 11 : Inference and future pathways: reflections and exploration of new horizons - p. Page 242 21. Bibliography - p. Page 262 22. Index - p. Page 262
This book 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
1. Iris and Periocular Recognition using Deep Learning - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page vi 7. Preface - p. Page vi 8. List of Illustrations - p. Page x 9. List of Tables - p. Page xi 10. 1 : An insight into trends on advances in iris and periocular recognition - p. Page xi 11. 2 : Unlocking the full potential of Iris recognition with deep learning - p. Page 32 12. 3 : A unified framework for accurate detection, segmentation, and recognition of irises - p. Page 60 13. 4 : Enhancing iris recognition accuracy through dilated residual features - p. Page 75 14. Chapter 5 : Iris recognition with deep learning across spectrums - p. Page 95 15. 6 : Semantics-assisted convolutional neural network for accurate periocular recognition - p. Page 122 16. 7 : Deep neural network with focused attention on critical periocular regions - p. Page 144 17. 8 : Dynamic iris recognition through multifeature collaboration - p. Page 169 18. 9 : Position-specific convolutional neural network to accurately match iris and periocular images - p. Page 193 19. 10 : Biometrics security in the metaverse using egocentric iris recognition - p. Page 210 20. 11 : Inference and future pathways: reflections and exploration of new horizons - p. Page 242 21. Bibliography - p. Page 262 22. Index - p. Page 262
