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2017 | OriginalPaper | Buchkapitel

Uncooperative Gait Recognition Using Joint Bayesian

verfasst von : Chao Li, Kan Qiao, Xin Min, Xiaoyan Pang, Shouqian Sun

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

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Abstract

Human gait, as a soft biometric, helps to recognize people by walking without subject cooperation. In this paper, we propose a more challenging uncooperative setting under which views of the gallery and probe are both unknown and mixed up (uncooperative setting). Joint Bayesian is adopted to model the view variance. We conduct experiments to evaluate the effectiveness of Joint Bayesian under the proposed uncooperative setting on OU-ISIR Large Population Dataset (OULP) and CASIA-B Dataset (CASIA-B). As a result, we confirm that Joint Bayesian significantly outperform the state-of-the-art methods for both identification and verification tasks even when the training subjects are different from the test subjects. For further comparison, the uncooperative protocol, experimental results, learning models, and test codes are available.

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Vorheriges Kapitel Abnormal Gait Detection in Surveillance Videos with FFT-Based Analysis on Walking Rhythm

Nächstes Kapitel Pedestrian Detection via Structure-Sensitive Deep Representation Learning

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Titel: Uncooperative Gait Recognition Using Joint Bayesian
verfasst von: Chao Li
Kan Qiao
Xin Min
Xiaoyan Pang
Shouqian Sun
Verlag: Springer International Publishing
Buch: Image and Graphics
Print ISBN: 978-3-319-71606-0

Electronic ISBN: 978-3-319-71607-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-71607-7_11

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