A Strong and Robust Skeleton-Based Gait Recognition Method with Gait Periodicity Priors

Gait recognition aims to identify people by their walking patterns. Normal human walking is a periodic movement, however, existing gait recognition methods rarely make use of gait periodicity. In this paper, we propose the gait Periodicity-inspired Temporal feature Pyramid aggregator (PTP), which introduces gait periodicity priors into gait feature extraction, resulting in a strong and robust skeleton-based gait recognition method called CycleGait. Specifically, inspired by gait periodicity, PTP adopts multiple parallel temporal convolution operators with pyramid temporal kernel sizes to extract temporal gait features. Then, PTP cooperates with the spatial Graph Convolutional Network (GCN) to form the GCN-PTP network. CycleGait uses this network to extract spatio-temporal gait features from a sequence of skeleton coordinates. In addition, to improve CycleGait's robustness and performance, we feed more gait samples with various gait cycles into CycleGait with the plug-and-play Irregular Pace Converter (IPC), which can automatically convert normal pace into irregular and reasonable paces. Extensive experiments conducted on the CASIA-B dataset and OG RGB+D dataset show that CycleGait has excellent performance in various complex scenarios, namely, cross-view and cross-walking conditions, and becomes one of the best SOTA methods, which not only outperforms the best preexisting gait recognition methods by a large margin but also exhibits a significant level of robustness.