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.

Gait, as one of unique biometric features, has the advantage of being recognized from a long distance away, can be widely used in public security. Considering 3D pose estimation is more challenging than 2D pose estimation in practice , we research on using 2D joints to recognize gait in this paper, and a new model-based gait recognition method JointsGait is put forward to extract gait information from 2D human body joints. Appearance-based gait recognition algorithms are prevalent before. However, appearance features suffer from external factors which can cause drastic appearance variations, e.g. clothing. Unlike previous approaches, JointsGait firstly extracted spatio-temporal features from 2D joints using gait graph convolutional networks, which are less interfered by external factors. Secondly, Joints Relationship Pyramid Mapping (JRPM) are proposed to map spatio-temporal gait features into a discriminative feature space with biological advantages according to the relationship of human joints when people are walking at various scales. Finally, we design a fusion loss strategy to help the joints features to be insensitive to cross-view. Our method is evaluated on two large datasets, Kinect Gait Biometry Dataset and CASIA-B. On Kinect Gait Biometry Dataset database, JointsGait only uses corresponding 2D coordinates of joints, but achieves satisfactory recognition accuracy compared with those model-based algorithms using 3D joints. On CASIA-B database, the proposed method greatly outperforms advanced model-based methods in all walking conditions, even performs superior to state-of-art appearance-based methods when clothing seriously affect people's appearance. The experimental results demonstrate that JointsGait achieves the state-of-art performance despite the low dimensional feature (2D body joints) and is less affected by the view variations and clothing variation.