Adaptive Biometric Systems: Recent Advances and Challenges - Rilegato

Rilegato

ISBN 10: 3319248634 ISBN 13: 9783319248639

Casa editrice: Springer-Nature New York Inc, 2015

Riassunto

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.

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Informazioni sull?autore

Dr. 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.

Dalla quarta di copertina

This timely and 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.

Topics and features:

• Presents a thorough introduction to the concept of adaptive biometric systems, detailing their taxonomy, levels of adaptation, and open issues and challenges
  
• 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

This comprehensive text/reference will be of great interest to researchers and practitioners engaged in systems science, information security or biometrics. Postgraduate and final-year undergraduate

students of computer engineering will also appreciate the coverage of intelligent and adaptive schemes for cutting-edge pattern recognition and signal processing in changing environments.

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2015
• Lingua: Inglese
• ISBN 10: 3319248634
• ISBN 13: 9783319248639
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 144
• Redattore: Rattani Ajita, Roli Fabio, Granger Eric
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania