Gait analysis leverages unique walking patterns for person identification and assessment across multiple domains. Among the methods used for gait analysis, skeleton-based approaches have shown promise due to their robust and interpretable features. However, these methods often rely on hand-crafted spatial-temporal graphs that are based on human anatomy disregarding the particularities of the dataset and task. This paper proposes a novel method to simplify the spatial-temporal graph representation for gait-based gender estimation, improving interpretability without losing performance. Our approach employs two models, an upstream and a downstream model, that can adjust the adjacency matrix for each walking instance, thereby removing the fixed nature of the graph. By employing the Straight-Through Gumbel-Softmax trick, our model is trainable end-to-end. We demonstrate the effectiveness of our approach on the CASIA-B dataset for gait-based gender estimation. The resulting graphs are interpretable and differ qualitatively from fixed graphs used in existing models. Our research contributes to enhancing the explainability and task-specific adaptability of gait recognition, promoting more efficient and reliable gait-based biometrics.

翻译：步态分析利用独特的行走模式在多个领域进行身份识别与评估。在步态分析方法中，基于骨骼的方法因其鲁棒且可解释的特征展现出潜力。然而，这类方法通常依赖于基于人体解剖学构建的手工设计时空图，忽略了数据集与任务的特殊性。本文提出了一种新颖方法，用于简化基于步态性别估计的时空图表示，在保持性能的同时提升可解释性。我们的方法采用两个模型（上游模型与下游模型），可针对每个行走实例调整邻接矩阵，从而消除图的固定特性。通过运用直通式Gumbel-Softmax技巧，该模型可实现端到端训练。我们在CASIA-B数据集上针对步态性别估计任务验证了方法的有效性。所生成的图具有可解释性，并在性质上区别于现有模型中的固定图结构。本研究有助于增强步态识别的可解释性与任务适应性，推动更高效可靠的步态生物特征识别技术发展。