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.

该论文提出了一种新方法，简化步态基础性别估计的空间-时间图表示，提高了解释性，同时没有损失性能。通过使用直通Gumbel-Softmax技巧，我们的模型可进行端到端训练。在CASIA-B数据集上验证了我们方法的有效性，得到的图形可解释性强，与现有模型中使用的固定图形有明显差异。我们的研究促进了步态识别的可解释性和任务特定适应性，推动了更高效可靠的基于步态的生物识别技术。

学习简化步态分析中的时空图