This interdisciplinary volume presents a detailed overview of the latest advances and challenges remaining in the field of adaptive biometric systems. A broad range of techniques are provided from an international selection of pre-eminent authorities, collected together under a unified taxonomy and designed to be applicable to any pattern recognition system. Features: presents a thorough introduction to the concept of adaptive biometric systems; reviews systems for adaptive face recognition that perform self-updating of facial models using operational (unlabeled) data; describes a novel semi-supervised training strategy known as fusion-based co-training; examines the characterization and recognition of human gestures in videos; discusses a selection of learning techniques that can be applied to build an adaptive biometric system; investigates procedures for handling temporal variance in facial biometrics due to aging; proposes a score-level fusion scheme for an adaptive multimodal biometric system.
表中的内容
Introduction to Adaptive Biometric Systems.- Context-Sensitive Self-Updating for Adaptive Face Recognition.- Handling Session Mismatch by Semi-Supervised Based Co-Training Scheme.- A Hybrid CRF/HMM for One-Shot Gesture Learning.- An Online Learning-Based Adaptive Biometric System.- Adaptive Facial Recognition Under Aging Effect.- An Adaptive Score Level Fusion Scheme for Multimodal Biometric Systems.
关于作者
Dr. Ajita Rattani is a post-doctoral fellow in the Integrated Pattern Recognition and Biometrics (i-PRo Be) lab at Michigan State University, East Lansing, MI, USA. Dr. Fabio Roli is a professor of computer engineering and the Director of the Pattern Recognition and Applications (PRA) lab at the University of Cagliari, Italy. Dr. Eric Granger is a professor in the Department of Automated Manufacturing Engineering and the Director of the Laboratory for Imagery, Vision and Artificial Intelligence at the École de technologie supérieure (ÉTS), Montréal, QC, Canada.
来自同一作者的更多电子书 / 编辑
16,913 此类电子书
