Jiang R., Chang-Tsun Li. Deep Biometrics

Springer, 2020. — 322 p. — (Unsupervised and Semi-Supervised Learning). — ISBN: 9783030325824, EISBN: 9783030325831.This book highlights new advances in biometrics using Deep Learning toward a deeper and broader 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 emerging new areas such as privacy and security issues, cancellable biometrics, soft biometrics, smart cities, big biometric data, biometric banking, medical biometrics, healthcare biometrics, 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 more profound and broader applications.Highlights the impact of deep learning in the field of biometrics in a wide area;
Exploits the more profound 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, the internet of things, and cloud computing.