  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.
