9783319616568-3319616560-Deep Learning for Biometrics (Advances in Computer Vision and Pattern Recognition)

Deep Learning for Biometrics (Advances in Computer Vision and Pattern Recognition)

ISBN-13: 9783319616568
ISBN-10: 3319616560
Edition: 1st ed. 2017
Publication date: 2017
Publisher: Springer
Format: Hardcover 343 pages
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Book details

ISBN-13: 9783319616568
ISBN-10: 3319616560
Edition: 1st ed. 2017
Publication date: 2017
Publisher: Springer
Format: Hardcover 343 pages

Summary

Acknowledged author wrote Deep Learning for Biometrics (Advances in Computer Vision and Pattern Recognition) comprising 343 pages back in 2017. Textbook and eTextbook are published under ISBN 3319616560 and 9783319616568. Since then Deep Learning for Biometrics (Advances in Computer Vision and Pattern Recognition) textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined.

Topics and features: addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities; revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition; examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition; discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition; investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples; presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories.Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning.
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