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Cross-view gait recognition using joint Bayesian
Author(s): Chao Li; Shouqian Sun; Xiaoyu Chen; Xin Min
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Paper Abstract

Human gait, as a soft biometric, helps to recognize people by walking. To further improve the recognition performance under cross-view condition, we propose Joint Bayesian to model the view variance. We evaluated our prosed method with the largest population (OULP) dataset which makes our result reliable in a statically way. As a result, we confirmed our proposed method significantly outperformed state-of-the-art approaches for both identification and verification tasks. Finally, sensitivity analysis on the number of training subjects was conducted, we find Joint Bayesian could achieve competitive results even with a small subset of training subjects (100 subjects). For further comparison, experimental results, learning models, and test codes are available.

Paper Details

Date Published: 21 July 2017
PDF: 6 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042009 (21 July 2017); doi: 10.1117/12.2281536
Show Author Affiliations
Chao Li, Zhejiang Univ. (China)
Shouqian Sun, Zhejiang Univ. (China)
Xiaoyu Chen, Zhejiang Univ. (China)
Xin Min, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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