Skeleton-Based Gait Recognition Based on Deep Neuro-Fuzzy Network

Gait recognition aims to identify users by their walking patterns. Compared with appearance-based methods, skeleton-based methods exhibit well robustness to cluttered backgrounds, carried items, and clothing variations. However, skeleton extraction faces the wrong human tracking and keypoints missing problems, especially under multiperson scenarios. To address above issues, this article proposes a novel gait recognition method using deep neural network specifically designed for multiperson scenarios. The method consists of individual gait separate module (IGSM) and fuzzy skeleton completion network (FU-SCN). To achieve effective human tracking, IGSM employs root–skeleton keypoints predictions and object keypoint similarity (OKS)-based skeleton calculation to separate individual gait sets when multiple persons exist. In addition, keypoints missing renders human poses estimation fuzzy. We propose FU-SCN, a deep neuro-fuzzy network, to enhances the interpretability of the fuzzy pose estimation via generating fine-grained gait representation. FU-SCN utilizes fuzzy bottleneck structure to extract features on low-dimension keypoints, and multiscale fusion to extract dissimilar relations of human body during walking on each scale. Extensive experiments are conducted on the CASIA-B dataset and our multigait dataset. The results show that our method is one of the SOTA methods and shows outperformance under complex scenarios. Compared with PTSN, PoseMapGait, JointsGait, GaitGraph2, and CycleGait, our method achieves an average accuracy improvement of 53.77%, 42.07%, 25.3%, 13.47%, and 9.5%, respectively, and it keeps low time cost with average 180 ms using edge devices.