Deep Biometrics (Paperback, 1st ed. 2020)

This book highlights new advances in biometrics using deep learning toward deeper and wider background, deeming it "Deep Biometrics". The book aims to highlight recent developments in biometrics using semi-supervised and unsupervised methods such as Deep Neural Networks, Deep Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary Networks, and so on. The contributors demonstrate the power of deep learning techniques in the emerging new areas such as privacy and security issues, cancellable biometrics, soft biometrics, smart cities, big biometric data, biometric banking, medical biometrics, healthcare biometrics, and biometric genetics, etc. The goal of this volume is to summarize the recent advances in using Deep Learning in the area of biometric security and privacy toward deeper and wider applications. Highlights the impact of deep learning over the field of biometrics in a wide area; Exploits the deeper and wider background of biometrics, such as privacy versus security, biometric big data, biometric genetics, and biometric diagnosis, etc.; Introduces new biometric applications such as biometric banking, internet of things, cloud computing, and medical biometrics.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Unsupervised and Semi-Supervised Learning
Release date:	August 2021
First published:	2020
Editors:	Richard Jiang • Chang-Tsun Li • Danny Crookes • Weizhi Meng • Christophe Rosenberger
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	320
Edition:	1st ed. 2020
ISBN-13:	978-3-03-032585-5
Categories:	Books > Science & Mathematics > Biology, life sciences > Molecular biology Books > Computing & IT > Computer communications & networking > Network security Books > Computing & IT > Applications of computing > Pattern recognition Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General Promotions
LSN:	3-03-032585-7
Barcode:	9783030325855

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