21 July 2017 Cross-view gait recognition using joint Bayesian
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042009 (2017) https://doi.org/10.1117/12.2281536
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
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.
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Chao Li, Chao Li, Shouqian Sun, Shouqian Sun, Xiaoyu Chen, Xiaoyu Chen, Xin Min, Xin Min, } "Cross-view gait recognition using joint Bayesian", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042009 (21 July 2017); doi: 10.1117/12.2281536; https://doi.org/10.1117/12.2281536
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