Machine Learning for Biometrics

Biometrics is essentially a pattern recognition technique. It has four major parts: biometric trait detection, preprocessing, feature extraction, and recognition. In each part 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. Machine Learning for Biometrics: Concepts, Algorithms and Applications highlights the fundamental concepts of machine learning, processing and analyzing data from biometrics, followed by 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. This book elucidates various biometric concepts, algorithms and applications with machine intelligence solutions. It would provide guidance on the best practices for new technologies such as e-health solutions, Data science, Cloud computing, and Internet of Things, etc. Covers different Machine Intelligence concepts, algorithms, and applications in the field of Cyber security, e-health monitoring, secure Cloud computing, secure IOT based operationsExplores advanced approaches to improve recognition performance of the biometric systems with use of recent Machine Intelligence techniquesIntroduces detection or segmentation technique to detect biometric characteristic from background in the input sample