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.

Developments in deep learning in the past decade have led to phenomenal growth in AI-based automated medical diagnosis, opening a door to a new era of both medical research and medical industry. It is a golden age for researchers involved in the development and application of advanced machine learning techniques for medical and clinical problems. This book captures the most recent important advances in this cross-disciplinary topic and brings the latest advances to a wide audience including experts, researchers, students, industry developers and medical services.

This book highlights recent research advances on biometrics using new methods such as deep learning, nonlinear graph embedding, fuzzy approaches, and ensemble learning. Included are special biometric technologies related to privacy and security issues, such as cancellable biometrics and soft biometrics. The book also focuses on several emerging topics such as big data issues, internet of things, medical biometrics, healthcare, and robot-human interactions. The authors show how these new applications have triggered a number of new biometric approaches. They show, as an example, how fuzzy extractor has become a useful tool for key generation in biometric banking, and vein/heart rates from medical records can also be used to identify patients. The contributors cover the topics, their methods, and their applications in depth.

This book highlights recent advances in smart cities technologies, with a focus on new technologies such as biometrics, blockchains, data encryption, data mining, machine learning, deep learning, cloud security, and mobile security. During the past five years, digital cities have been emerging as a technology reality that will come to dominate the usual life of people, in either developed or developing countries. Particularly, with big data issues from smart cities, privacy and security have been a widely concerned matter due to its relevance and sensitivity extensively present in cybersecurity, healthcare, medical service, e-commercial, e-governance, mobile banking, e-finance, digital twins, and so on. These new topics rises up with the era of smart cities and mostly associate with public sectors, which are vital to the modern life of people. This volume summarizes the recent advances in addressing the challenges on big data privacy and security in smart cities and points out the future research direction around this new challenging topic.

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.

This book highlights recent research advances on biometrics using new methods such as deep learning, nonlinear graph embedding, fuzzy approaches, and ensemble learning. Included are special biometric technologies related to privacy and security issues, such as cancellable biometrics and soft biometrics. The book also focuses on several emerging topics such as big data issues, internet of things, medical biometrics, healthcare, and robot-human interactions. The authors show how these new applications have triggered a number of new biometric approaches. They show, as an example, how fuzzy extractor has become a useful tool for key generation in biometric banking, and vein/heart rates from medical records can also be used to identify patients. The contributors cover the topics, their methods, and their applications in depth.