Machine Learning for Biometrics: Concepts, Algorithms and Applications highlights the fundamental concepts of machine learning, processing and analyzing data from biometrics and provides a review of intelligent and cognitive learning tools which can be adopted in this direction. Each chapter of the volume is supported by real-life case studies, illustrative examples and video demonstrations. The book elucidates various biometric concepts, algorithms and applications with machine intelligence solutions, providing guidance on best practices for new technologies such as e-health solutions, Data science, Cloud computing, and Internet of Things, etc.
In each section, different machine learning concepts and algorithms are used, such as different object detection techniques, image enhancement techniques, both global and local feature extraction techniques, and classifiers those are commonly used data science techniques. These biometrics techniques can be used as tools in Cloud computing, Mobile computing, IOT based applications, and e-health care systems for secure login, device access control, personal recognition and surveillance.
1. Machine Learning for Biometrics - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xiii 7. Preface - p. xv 8. Acknowledgments - p. xix 9. Chapter 1 Machine learning approach for longitudinal face recognition of children - p. 1 10. Chapter 2 Thermal biometric face recognition (TBFR): A noncontact face biometry - p. 29 11. Chapter 3 Multimodal biometric recognition using human ear and profile face: An improved approach - p. 47 12. Chapter 4 Statistical measures for Palmprint image enhancement - p. 65 13. Chapter 5 Retina biometrics for personal authentication - p. 87 14. Chapter 6 Gender recognition from facial images using multichannel deep learning framework - p. 105 15. Chapter 7 Implementation of cardiac signal for biometric recognition from facial video - p. 129 16. Chapter 8 Real-time emotion engagement tracking of students using human biometric emotion intensities - p. 143 17. Chapter 9 Facial identification expression-based attendance monitoring and emotion detection—A deep CNN approach - p. 155 18. Chapter 10 Contemporary survey on effectiveness of machine and deep learning techniques for cyber security - p. 177 19. Chapter 11 A secure biometric authentication system for smart environment using reversible data hiding through encryption scheme - p. 201 20. Chapter 12 An efficient and untraceable authentication protocol for cloud-based healthcare system - p. 217 21. Index - p. 237
Machine Learning for Biometrics: Concepts, Algorithms and Applications highlights the fundamental concepts of machine learning, processing and analyzing data from biometrics and provides a review of intelligent and cognitive learning tools which can be adopted in this direction. Each chapter of the volume is supported by real-life case studies, illustrative examples and video demonstrations. The book elucidates various biometric concepts, algorithms and applications with machine intelligence solutions, providing guidance on best practices for new technologies such as e-health solutions, Data science, Cloud computing, and Internet of Things, etc.
In each section, different machine learning concepts and algorithms are used, such as different object detection techniques, image enhancement techniques, both global and local feature extraction techniques, and classifiers those are commonly used data science techniques. These biometrics techniques can be used as tools in Cloud computing, Mobile computing, IOT based applications, and e-health care systems for secure login, device access control, personal recognition and surveillance.
1. Machine Learning for Biometrics - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xiii 7. Preface - p. xv 8. Acknowledgments - p. xix 9. Chapter 1 Machine learning approach for longitudinal face recognition of children - p. 1 10. Chapter 2 Thermal biometric face recognition (TBFR): A noncontact face biometry - p. 29 11. Chapter 3 Multimodal biometric recognition using human ear and profile face: An improved approach - p. 47 12. Chapter 4 Statistical measures for Palmprint image enhancement - p. 65 13. Chapter 5 Retina biometrics for personal authentication - p. 87 14. Chapter 6 Gender recognition from facial images using multichannel deep learning framework - p. 105 15. Chapter 7 Implementation of cardiac signal for biometric recognition from facial video - p. 129 16. Chapter 8 Real-time emotion engagement tracking of students using human biometric emotion intensities - p. 143 17. Chapter 9 Facial identification expression-based attendance monitoring and emotion detection—A deep CNN approach - p. 155 18. Chapter 10 Contemporary survey on effectiveness of machine and deep learning techniques for cyber security - p. 177 19. Chapter 11 A secure biometric authentication system for smart environment using reversible data hiding through encryption scheme - p. 201 20. Chapter 12 An efficient and untraceable authentication protocol for cloud-based healthcare system - p. 217 21. Index - p. 237
