SPD-Net: A semantic partitioned transformer with dynamic graph network for improved skeleton-based gait recognition

Abstract

Gait recognition has gained prominence as a biometric modality owing to its unobtrusive and non-invasive nature. Existing methods primarily rely on silhouette-based representations, making them sensitive to variations in clothing, occlusion, and background noise. In contrast, model-based approaches utilize skeleton sequences to capture motion dynamics through joint connectivity, thereby reducing dependence on visual appearance. However, these approaches often rely on physically connected joints, limiting their ability to model semantically meaningful joint relationships. Transformer-based models mitigate this limitation by capturing long-range dependencies, but at the expense of substantial computational overhead. To address these challenges, this work proposes the Semantic Partitioned transformer with Dynamic Graph Network (SPD-Net) for robust gait recognition. SPD-Net integrates Dynamic Graph Convolutional Network (DGCN), Temporal Convolutional Network (TCN), and Semantic Partitioned Multi-head Self-Attention (SP-MSA) to enhance the representation of gait features. DGCN dynamically learns spatial correlations between joints, while TCN captures temporal dependencies. Furthermore, SP-MSA introduces a semantic partitioning strategy that selectively focuses on key joints and frames, significantly reducing computational complexity while preserving crucial gait patterns. This approach effectively models both physically neighboring and distant joint relationships, along with intra- and inter-frame correlations. Finally, a Joint-Part Mapping (JPM) module enhances the discriminative power of gait representations by capturing hierarchical joint relationships across multiple scales. Experimental evaluations on benchmark gait datasets show that SPD-Net surpasses prior state-of-the-art approaches, achieving improved robustness and accuracy across diverse gait recognition challenges.