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
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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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