Face Recognition Across The Imaging Spectrum
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Face Recognition Across the Imaging Spectrum
Author: Thirimachos Bourlai
language: en
Publisher: Springer Nature
Release Date: 2024-10-03
Embark on a journey through the recent groundbreaking developments in face recognition (FR) systems with our second edition. Initially designed for controlled conditions, FR systems have evolved to conquer real-world challenges, adapting to low-light scenarios and extended distances. Our book delves into the transformation brought about by advancements in imaging sensors and cost-effective infrared (IR) cameras, exploring intensified near-infrared (NIR), shortwave IR (SWIR), middle-wave IR (MWIR), and long-wave IR (LWIR) imagery. This edition caters to the burgeoning interest in FR technologies, aligning with recent strides in computer vision, pattern recognition, and biometric analysis. Tailored for biometrics researchers, practitioners, and students, it addresses the critical need for FR algorithms in operational environments. Our book encompasses three comprehensive sections: (a) Face Recognition and Biometric Systems: Dive into topics such as face profile, facial attractiveness, periocular and binocular recognition, and quality training for face-based examinations. (b) Biometric System Security and Attacks: Explore adversarial attacks, domain transformers, demographic fairness, ocular pathologies, and distance-based classification of biometric images. (c) Biometric Image Synthesis and Technology Enhancements:Uncover the secrets of face image synthesis, thermal band head pose estimation, facial image analysis in forensic examination, and optimal computer monitor configurations. With 13 meticulously crafted chapters, this edition provides updated insights, experimental findings, and a roadmap for the future. Each chapter delivers a rich exploration of its specific topic, weaving together background information, literature reviews, methodologies, experiments, and concluding with challenges and future directions. Elevate your understanding of evolving face recognition technologies – the future awaits!
Disease Control Through Social Network Surveillance
Author: Thirimachos Bourlai
language: en
Publisher: Springer Nature
Release Date: 2022-09-06
This book examines modern paradigms of disease control based on social network surveillance applications, including electronic sentinel surveillance and wireless application-based surveillance science. It also highlights topics that integrate statistical and epidemiological sciences with surveillance practice and, in order to reflect the evolution of social networking practices, discusses topics concerning the challenges for surveillance theory and practice. In turn, the book goes a step further by providing insights on how we need to analyse epidemiological trends by following best practices on distinguishing useful information from noise, namely fake news, false reporting of disease incidents and events, etc. At the same time, we need to be able to protect health-focused applications and communication tools via cybersecurity technologies and to ensure that anonymity of reporting and privacy are preserved. In closing, the book discusses the role and impact of social media on disease surveillance, as well as the current role of communities in infectious disease surveillance and control.
Deep Biometrics
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.