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151215 ||| eng |
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|a 9783319248653
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| 100 |
1 |
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|a Rattani, Ajita
|e [editor]
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| 245 |
0 |
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|a Adaptive Biometric Systems
|h Elektronische Ressource
|b Recent Advances and Challenges
|c edited by Ajita Rattani, Fabio Roli, Eric Granger
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| 250 |
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|a 1st ed. 2015
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2015, 2015
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| 300 |
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|a X, 134 p. 44 illus., 24 illus. in color
|b online resource
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Signal, Speech and Image Processing
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| 653 |
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|a Biometrics
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Signal processing
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| 653 |
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|a Automated Pattern Recognition
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| 653 |
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|a Biometric identification
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| 653 |
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|a Pattern recognition systems
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| 700 |
1 |
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|a Roli, Fabio
|e [editor]
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| 700 |
1 |
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|a Granger, Eric
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Advances in Computer Vision and Pattern Recognition
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| 028 |
5 |
0 |
|a 10.1007/978-3-319-24865-3
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-319-24865-3?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.248
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| 520 |
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|a 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
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