SATGCN: A Spatial Attention-Guided Temporal Graph Convolutional Network for Gait Recognition

Graph convolutional networks (GCNs) have revealed great promise in skeleton-based gait recognition by modeling human joints as a spatiotemporal graph. However, most current spatiotemporal GCN (ST-GCN) methods rely on a fixed graph topology and a static partitioning scheme. This fixed approach limits the model’s ability to capture complex relationships between distant body joints. Besides, Temporal Convolutional Networks (TCNs) are commonly used to model temporal dynamics. However, they have shortcomings in capturing temporal patterns at multiple scales. To overcome these constraints, this paper introduces a new architecture, i.e., SATGCN, that integrates a graph attention combination module (GACM) with a multi-scale temporal convolutional network (MSTCN). In GACM, a fusion approach is used, in which the attention module dynamically learns joint relationships with GCN at each frame. It enables the network to capture local spatial topology and long-range dependencies between physically distant body parts. Following the spatial module, the multi-scale TCN analyzes joint features over time at multiple scales, capturing both short- and long-range motion patterns across the gait sequence. The model is evaluated on the CASIA-B gait recognition dataset under normal, bag-carrying, and clothing conditions, achieving accuracies of 96.97%, 93.10%, and 90.65%, respectively. The performance is about 0.97%, 1.80%, and 0.65% better than that of previous works in the three conditions. The results reveal the effectiveness of combining an attention-guided GCN with multi-scale temporal convolution for skeleton-based gait recognition.