Author Description

Most biometric systems employed for human recognition require physical contact with, or close proximity to, a cooperative subject. Far more challenging is the ability to reliably recognize individuals at a distance, when viewed from an arbitrary angle under real-world environmental conditions. Gait and face data are the two biometrics that can be most easily captured from a distance using a video camera. This comprehensive and logically organized text/reference addresses the fundamental problems associated with gait and face-based human recognition, from color and infrared video data that are acquired from a distance. It examines both model-free and model-based approaches to gait-based human recognition, including newly developed techniques where the both the model and the data (obtained from multiple cameras) are in 3D. In addition, the work considers new video-based techniques for face profile recognition, and for the super-resolution of facial imagery obtained at different angles. Finally, the book investigates integrated systems that detect and fuse both gait and face biometrics from video data. Topics and features: Discusses a framework for human gait analysis based on Gait Energy Image, a spatio-temporal gait representation Evaluates the discriminating power of model-based gait features using Bayesian statistical analysis Examines methods for human recognition using 3D gait biometrics, and for moving-human detection using both color and thermal image sequences Describes approaches for the integration face profile and gait biometrics, and for super-resolution of frontal and side-view face images Introduces an objective non-reference quality evaluation algorithm for super-resolved images Presents performance comparisons between different biometrics and different fusion methods for integrating gait and super-resolved face from video This unique and authoritative text is an i