Salvato in:
| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.12047 |
| Tags: |
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Sommario:
- 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.