Academic Press Machine Learning for Biometrics: Concepts, Algorithms and Applications (Cognitive Data Science in Sustainable Computing)

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. Review Provides fundamental information on the up-to-date concepts and algorithms of biometrics used in personal recognition From the Back Cover <p>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. </p><i> <p>Machine Learning for Biometrics: Concepts, Algorithms and Applications</p></i><p> 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.</p> <p>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.</p> About the Author Dr. Partha Pratim Sarangi, is working as Professor in Computer Sc. & Engineering at Seemanta Engineering College, Jharpokharia, Odisha, INDIA. He has received his Ph.D. in Computer Science from KIIT University, Bhubaneswar, Odisha. His current research interests include Pattern Recognition, Soft computing, Biometrics, Computer Vision, and Data Science. He has already published about 22 research papers in refereed journals and conferences. Ms. Madhumita Panda, is working as Assistant Professor in Master in Computer Applications at Seemanta Engineering College, Jharpokharia, Odisha. She has received his M.Tech in Computer Science from NIT Rourkela and continuing her Ph.D. from North Orissa University, Baripada, Odisha. Her current research interests include Pattern Recognition, Soft computing, Biometrics, Software Engineering. She has already published 2 book chapters and about 10 research papers in refereed journals and conferences. Dr. Subhashree Mishra, is working as an Assistant Professor in School of Electronics Engineering at KIIT University, Bhubaneswar. She has completed her Master and PhD from KIIT University respectively. Her research interest includes Machine Learning, Communication Engineering, Image Processing and Soft Computing. She has published