About the Book:
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.
Key Features
Covers different machine intelligence concepts, algorithms and applications in the field of cybersecurity, e-health monitoring, secure cloud computing and secure IOT based operations
Explores advanced approaches to improve recognition performance of biometric systems with the use of recent machine intelligence techniques
Introduces detection or segmentation techniques to detect biometric characteristics from the background in the input sample
Table of Contents
Chapter 1: Machine learning approach for longitudinal face recognition of children
Chapter 2: Thermal biometric face recognition (TBFR): A noncontact face biometry
Chapter 3: Multimodal biometric recognition using human ear and profile face: An improved approach
Chapter 4: Statistical measures for Palmprint image enhancement
Chapter 5: Retina biometrics for personal authentication
Chapter 6: Gender recognition from facial images using multichannel deep learning framework
Chapter 7: Implementation of cardiac signal for biometric recognition from facial video
Chapter 8: Real-time emotion engagement tracking of students using human biometric emotion intensities
Chapter 9: Facial identification expression-based attendance monitoring and emotion detection—A deep CNN approach
Chapter 10: Contemporary survey on effectiveness of machine and deep learning techniques for cyber security
Chapter 11: A secure biometric authentication system for smart environment using reversible data hiding through encryption scheme
Chapter 12: An efficient and untraceable authentication protocol for cloud-based healthcare system
