Publication: Robust gait recognition using hybrid descriptors based on Skeleton Gait Energy Image
Issued Date
2019-01-01
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ISSN
01678655
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2-s2.0-85065906849
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Mahidol University
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SCOPUS
Bibliographic Citation
Pattern Recognition Letters. (2019)
Suggested Citation
Lingxiang Yao, Worapan Kusakunniran, Qiang Wu, Jian Zhang, Zhenmin Tang, Wankou Yang Robust gait recognition using hybrid descriptors based on Skeleton Gait Energy Image. Pattern Recognition Letters. (2019). doi:10.1016/j.patrec.2019.05.012 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/50688
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Title
Robust gait recognition using hybrid descriptors based on Skeleton Gait Energy Image
Abstract
© 2019 Gait features have been widely applied in human identification. The commonly-used representations for gait recognition can be roughly classified into two categories: model-free features and model-based features. However, due to the view variances and clothes changes, model-free features are sensitive to the appearance changes. For model-based features, there is great difficulty in extracting the underlying models from gait sequences. Based on the confidence maps and the part affinity fields produced by a two-branch multi-stage CNN network, a new model-based representation, Skeleton Gait Energy Image (SGEI), has been proposed in this paper. Another contribution is that a hybrid representation has been produced, which uses SGEI to remedy the deficiency of model-free features, Gait Energy Image (GEI) for instance. The experimental performances indicate that our proposed methods are more robust to the cloth changes, and contribute to increasing the robustness of gait recognition in the unconstrained environments with view variances and clothes changes.