Gait recognition via weighted global-local feature fusion and attention-based multiscale temporal aggregation

Gait recognition is a biometric technology that distinguishes individuals by analyzing their walking patterns and holds significant potential for development. Current methods primarily focus on extracting gait features either from the overall appearance or specific local regions. However, they often overlook the partitioning of local regions based on the individual's body structure, as well as the weighted relationships between global and local features. We propose a Weighted Global-Local Feature Fusion Module to partition local features according to human body parts and adaptively integrate global and local gait features. This approach facilitates fine-grained learning of part-level local features and enhances the discriminative representation of gait features. Furthermore, we employ an Attention-based Multiscale Temporal Aggregation operation to adaptively fuse motion features from different time scales, preserving crucial spatio-temporal information while reducing the length of the time series. The average Rank-1 accuracy in CASIA-B and OUMVLP datasets is 93.0% and 90.7%, respectively. The experimental results demonstrate that our method achieves satisfactory recognition performance, indicating its potential for advancing gait recognition technology.