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OverviewThis 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. Full Product DetailsAuthor: Ajita Rattani , Fabio Roli , Eric GrangerPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: Softcover reprint of the original 1st ed. 2015 Weight: 2.292kg ISBN: 9783319372228ISBN 10: 331937222 Pages: 134 Publication Date: 23 August 2016 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsIntroduction 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.ReviewsAuthor InformationDr. Ajita Rattani is a post-doctoral fellow in the Integrated Pattern Recognition and Biometrics (i-PRoBe) 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. Tab Content 6Author Website:Countries AvailableAll regions |
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