ResGait: gait feature refinement based on residual structure for gait recognition

Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject's walking posture at a distance. However, a lot of redundant information in gait sequence will affect the performance of gait recognition, and the most existing gait recognition models are overly complicated and parameterized, which leads to the low efficiency in model training. Consequently, how to reduce the complexity of the model and eliminate redundant information effectively in gait have become a challenging problem in the field of gait recognition. In this paper, we present a residual structure based gait recognition model, short for ResGait, to learn the most discriminative changes of gait patterns. To eliminate redundant information in gait, the soft thresholding is inserted into the deep architectures as a nonlinear transformation layer to improve gait feature learning capability from the noised gait feature map. Moreover, each sample owns unique set of thresholds, making the proposed model suitable for different gait sequences with different redundant information. Furthermore, residual link is introduced to reduce the learning difficulties and alleviate computational costs in model training. Here, we train the network in terms of various scenarios and walking conditions, and the effectiveness of the method is validated through abundant experiments with various types of redundant information in gait. In comparison to the previous state-of-the-art works, experimental results on the common datasets, CASIA-B and OUMVLP-Pose, show that ResGait has higher recognition accuracy under various walking conditions and scenarios.