AI and Deep Learning in Biometric Security: Trends, Potential, and Challenges

This book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve biometric security problems, and highlights cutting-edge research on the use of convolution neural networks for biometrics authentication. It is a valuable resource for scholars, researchers, practitioners, and technology aspirants interested in AI and biometric security.

\n Format: Hardback
\n Length: 364 pages
\n Publication date: 22 March 2021
\n Publisher: Taylor & Francis Ltd
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This comprehensive book delves into the intricate realms of artificial intelligence and deep learning, offering a thorough exploration of their applications in solving biometric security-related challenges. Through a series of case studies, it provides in-depth insights into various aspects of biometric security, including authentication, indexing, template protection, spoofing attack detection, ROI detection, and gender classification.

The book showcases cutting-edge research on the utilization of convolutional neural networks, autoencoders, and recurrent convolutional neural networks in the analysis of face, hand, iris, gait, fingerprint, vein, and medical biometric traits. It also offers a step-by-step guide to understanding deep learning concepts for biometrics authentication approaches, while conducting an in-depth analysis of biometric images under various environmental conditions.

Designed to appeal to scholars, researchers, practitioners, and technology aspirants alike, this book serves as a valuable resource for those interested in delving into the field of AI and biometric security. It provides a comprehensive foundation for those seeking to advance their knowledge and expertise in this rapidly evolving domain.

\n Weight: 714g\n
Dimension: 161 x 241 x 28 (mm)\n
ISBN-13: 9780367422448\n \n