Gait coordination feature modeling and multi-scale gait representation for gait recognition

Gait recognition is an advanced biometric modality that identifies individuals based on their walking postures. Currently, the majority of gait recognition methods employ silhouette images as the primary representation of gait. However, the inclusion of silhouette information, which incorporates details about clothing and carried items, may interfere with the extraction of gait features. To this end, we present GaitSkeleton, a novel approach that utilizes skeleton sequences as the gait representation. GaitSkeleton models the motor coordination feature of gait and combines three-scale spatial features to obtain discriminative features for gait recognition. To capture the coordination relationship between joints, we design a Coordination Feature Learner (CFL). CFL is embedded into the ST-GCN block to enhance the model’s representational capacity. Additionally, we introduce a novel feature cascade module called MSCM, which further enhances the discriminative information by sampling spatial features at three scales. The entire network significantly enhances the discriminative nature of gait features. Experimental results on the CASIA-B dataset demonstrate that our method achieves state-of-the-art performance in model-based gait recognition.