Learning to Simplify Spatial-Temporal Graphs in Gait Analysis
Adrian Cosma Emilian Radoi
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.