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Titlebook: Adaptive Biometric Systems; Recent Advances and Ajita Rattani,Fabio Roli,Eric Granger Book 2015 Springer International Publishing Switzerl

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發(fā)表于 2025-3-21 17:05:55 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Adaptive Biometric Systems
期刊簡(jiǎn)稱Recent Advances and
影響因子2023Ajita Rattani,Fabio Roli,Eric Granger
視頻videohttp://file.papertrans.cn/145/144601/144601.mp4
發(fā)行地址The first book dedicated to the emerging field of adaptive biometric systems.Describes the schemes and learning mechanisms involved in biometric system adaptation, and provides insight into the levels
學(xué)科分類Advances in Computer Vision and Pattern Recognition
圖書封面Titlebook: Adaptive Biometric Systems; Recent Advances and  Ajita Rattani,Fabio Roli,Eric Granger Book 2015 Springer International Publishing Switzerl
影響因子.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..
Pindex Book 2015
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書目名稱Adaptive Biometric Systems影響因子(影響力)




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Bacterial Cell Walls and Surfacesso resolve the issue of how to select the decision threshold for adaptation. In particular, we find that a strong classifier , including a multimodal system, may benefit better from a more relaxed threshold, whereas a weak classifier may benefit better from a more stringent threshold.
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Context-Sensitive Self-Updating for Adaptive Face Recognition,978-3-658-22998-6
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