Abstract
Gait recognition has emerged as a robust biometric modality due to its non-intrusive nature and resilience to occlusion. Conventional gait recognition methods typically rely on silhouettes or skeletons. Despite their success in gait recognition for controlled laboratory environments, they usually fail in real-world scenarios due to their limited information entropy for gait representations. To achieve accurate gait recognition in the wild, we propose a novel gait representation, named Parsing Skeleton. This representation innovatively introduces the skeleton-guided human parsing method to capture fine-grained body dynamics, so they have much higher information entropy to encode the shapes and dynamics of fine-grained human parts during walking. Moreover, to effectively explore the capability of the parsing skeleton representation, we propose a novel parsing skeleton-based gait recognition framework, named PSGait, which takes parsing skeletons and silhouettes as input. By fusing these two modalities, the resulting image sequences are fed into gait recognition models for enhanced individual differentiation. We conduct comprehensive benchmarks on various datasets to evaluate our model. PSGait outperforms existing state-of-the-art multimodal methods. Furthermore, as a plug-and-play method, PSGait leads to a maximum improvement of 10.9% in Rank-1 accuracy across various gait recognition models. These results demonstrate the effectiveness and versatility of parsing skeletons for gait recognition in the wild, establishing PSGait as a new state-of-the-art approach for multimodal gait recognition.
Abstract (translated)
步态识别作为一种稳健的生物识别模式，由于其非侵入性和抗遮挡性而崭露头角。传统步态识别方法通常依赖于轮廓或骨架。尽管这些方法在受控实验室环境中取得了成功，但它们在现实世界场景中往往表现不佳，因为它们用来表示步态的信息熵非常有限。为了实现野外环境中的准确步态识别，我们提出了一种新颖的步态表示法，称为解析骨架（Parsing Skeleton）。这种表示通过引入骨骼引导的人体解析方法来捕捉细微的身体动态，从而显著提高了信息熵，能够更好地编码步行过程中人体各部分的具体形状和动态。 为了有效利用解析骨架表征的能力，我们提出了一个基于解析骨架的新型步态识别框架，命名为PSGait。该框架接受解析骨架和轮廓作为输入，并通过融合这两种模态的信息来提高图像序列在步态识别模型中的个体区分度。我们在多种数据集上进行了全面基准测试以评估我们的模型性能。结果表明，PSGait超越了现有的最先进的多模式方法。 此外，作为一种即插即用的方法，PSGait在各种步态识别模型中实现了最高达10.9%的Rank-1准确率提升。这些结果证明了解析骨架对于野外环境中的步态识别的有效性和灵活性，并将PSGait确立为新的多模态步态识别前沿方法。
URL
https://arxiv.org/abs/2503.12047
