An Overview of Spectral Imaging of Human Skin toward Face Recognition -- Collection of Multi-Spectral Biometric Data for Cross-Spectral Identification Applications -- Hyperspectral Face Databases for Facial Recognition Research -- MWIR-to-Visible and LWIR-to-Visible Face Recognition Using Partial Least Squares and Dictionary Learning -- Local Operators and Measures for Heterogeneous Face Recognition -- Assessment of Facial Recognition System Performance in Realistic Operating Environments -- Understanding Thermal Face Detection: Challenges and Evaluation -- Face Recognition Systems under Spoofing Attacks -- for Biometric Applications -- Multispectral Ocular Biometrics.

This authoritative text/reference presents a comprehensive review of algorithms and techniques for face recognition (FR), with an emphasis on systems that can be reliably used in operational environments. Insights are provided by an international team of pre-eminent experts into the processing of multispectral and hyperspectral face images captured under uncontrolled environments. These discussions cover a variety of imaging sensors ranging from state-of-the-art visible and infrared imaging sensors, to RGB-D and mobile phone image sensors. A range of different biometric modalities are also examined, including face, periocular and iris. Topics and features: Presents a detailed introduction to the spectral imaging of human skin, and describes the data collection of multispectral and hyperspectral face images Covers a range of face-based identification technologies addressing challenges in the processing of face images in visible and infrared spectral bands Provides an overview of spoofing attacks and counter-measures for FR systems, and analyzes the effects of digital image alterations on FR algorithms Investigates the processing of degraded face photographs from ID documents, and examines mobile FR in still images and video sequences Proposes a state-of-the-art RGB-D FR algorithm, and suggests algorithms for 3D-based FR as well as techniques blending 2D and 3D data Discusses score normalization methods, and explores the use of patterns in the sclera in order to improve the robustness of iris recognition systems This timely volume is a mine of useful information for researchers, practitioners and students involved in image processing, computer vision, biometrics and security. Dr. Thirimachos Bourlai is an Assistant Professor in the Lane Department of Computer Science and Electrical Engineering at West Virginia University, Morgantown, WV, USA.