<p>Gait recognition technology represents a promising biometric identification method with extensive applications in fields such as visual surveillance. However, addressing the few-shot learning challenge remains an urgent issue in practical applications. In this paper, we propose a novel Dual-Scale Spatial-Temporal Aggregation Network (DSAN), which extracts gait features at different temporal scales through a dual-branch architecture. The Multi-Scale Temporal Aggregation Module (MSTA) is designed to aggregate multi-scale local temporal information from long-term gait features, employing an attention mechanism to reduce redundant information and enhance temporal feature representation. Additionally, the Covariance-guided Spatial-Temporal Attention Module (CoSTA) integrates short-term features into long-term representations through global covariance pooling and attention mechanisms, further improving feature expressiveness. Furthermore, we introduce the View Variation Adversarial Normalization (VVAN), which mitigates cross-view discrepancies under few-shot conditions via adversarial learning. Different from GaitGL, DSAN explicitly captures long-term and short-term dynamics and performs cross-scale fusion via MSTA and CoSTA. In contrast to GaitDAN’s multi-discriminator formulation, VVAN employs a single multi-class view discriminator, requiring only a change in the classifier output dimension when the view set varies. Extensive experiments are conducted on the CASIA-B and OUMVLP datasets, with DSAN evaluated in a one-shot setting on CASIA-B. The experimental results demonstrate that DSAN achieves state-of-the-art performance in gait recognition tasks and effectively alleviates the adverse impact of the one-shot problem on recognition accuracy.</p>