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.

翻译：步态识别因其非侵入性特点，已成为一种稳健的生物特征识别方式。传统的步态识别方法主要依赖于轮廓或骨架。尽管这些方法在受控实验室环境中表现有效，但其有限的信息熵限制了其在真实场景中的泛化能力。为克服这一局限，我们提出了一种称为**解析骨架**的新型表示方法，该方法利用骨架引导的人体解析技术，以更高的信息熵捕捉细粒度的身体动态。为充分挖掘解析骨架的潜力，我们还提出了**PSGait**框架，该框架将解析骨架与轮廓信息融合，以增强个体区分度。全面的基准测试表明，PSGait在显著减少计算资源的同时，性能优于当前最先进的多模态方法。作为一种即插即用方法，它在多种模型中将Rank-1准确率最高提升了15.7%。这些结果验证了解析骨架作为一种**轻量级**、**高效**且具有高度**泛化性**的表示方法，适用于野外环境下的步态识别。代码可在 https://github.com/realHarryX/PSGait 获取。