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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.12047 |
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| _version_ | 1866914342647103488 |
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| author | Xu, Hangrui Wu, Zhengxian Zhang, Chuanrui Chen, Zhuohong Liu, Zhifang Jiao, Peng Wang, Haoqian |
| author_facet | Xu, Hangrui Wu, Zhengxian Zhang, Chuanrui Chen, Zhuohong Liu, Zhifang Jiao, Peng Wang, Haoqian |
| contents | Gait recognition has emerged as a robust biometric modality due to its non-intrusive nature. Conventional gait recognition methods mainly rely on silhouettes or skeletons. While effective in controlled laboratory settings, their limited information entropy restricts generalization to real-world scenarios. To overcome this, we propose a novel representation called \textbf{Parsing Skeleton}, which uses a skeleton-guided human parsing method to capture fine-grained body dynamics with much higher information entropy. To effectively explore the capability of the Parsing Skeleton, we also introduce \textbf{PSGait}, a framework that fuses Parsing Skeleton with silhouettes to enhance individual differentiation. Comprehensive benchmarks demonstrate that PSGait outperforms state-of-the-art multimodal methods while significantly reducing computational resources. As a plug-and-play method, it achieves an improvement of up to 15.7\% in the accuracy of Rank-1 in various models. These results validate the Parsing Skeleton as a \textbf{lightweight}, \textbf{effective}, and highly \textbf{generalizable} representation for gait recognition in the wild. Code is available at https://github.com/realHarryX/PSGait. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12047 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PSGait: Gait Recognition using Parsing Skeleton Xu, Hangrui Wu, Zhengxian Zhang, Chuanrui Chen, Zhuohong Liu, Zhifang Jiao, Peng Wang, Haoqian Computer Vision and Pattern Recognition Gait recognition has emerged as a robust biometric modality due to its non-intrusive nature. Conventional gait recognition methods mainly rely on silhouettes or skeletons. While effective in controlled laboratory settings, their limited information entropy restricts generalization to real-world scenarios. To overcome this, we propose a novel representation called \textbf{Parsing Skeleton}, which uses a skeleton-guided human parsing method to capture fine-grained body dynamics with much higher information entropy. To effectively explore the capability of the Parsing Skeleton, we also introduce \textbf{PSGait}, a framework that fuses Parsing Skeleton with silhouettes to enhance individual differentiation. Comprehensive benchmarks demonstrate that PSGait outperforms state-of-the-art multimodal methods while significantly reducing computational resources. As a plug-and-play method, it achieves an improvement of up to 15.7\% in the accuracy of Rank-1 in various models. These results validate the Parsing Skeleton as a \textbf{lightweight}, \textbf{effective}, and highly \textbf{generalizable} representation for gait recognition in the wild. Code is available at https://github.com/realHarryX/PSGait. |
| title | PSGait: Gait Recognition using Parsing Skeleton |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.12047 |