Multiview Gait Recognition on Unconstrained Path Using Graph Convolutional Neural Network

Human gait recognition is a valuable biometric trait with vast applications in security domain. In most situations, the gait data is collected while the subject walks straight. Thus, the performance of the gait recognition system degrades when the subject changes walking direction. Previous gait recognition research was predominantly conducted for constrained paths, which limited the system's robustness and applicability. This paper introduces a novel approach for gait recognition which aims to recognize subjects walking along an unconstrained path. A graph neural network-based method is proposed for gait recognition along unconstrained path. The input of the architecture is the body joint coordinates and adjacency matrix representing the skeleton joints. Furthermore, a residual connection is incorporated to produce a smoothened output of the input feature. This graph neural network model utilizes the kinematic relationships of the body joints as well as spatial and temporal features. The findings demonstrate that the proposed method outperformed other state-of-the-art gait recognition methods on unconstrained paths. Multi-view Gait AVA and CASIA-B dataset are used to evaluate the efficacy of the proposed method.