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

Abstract

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