Gait recognition based on 3D skeleton joints captured by kinect

Abstract

2D-video-based gait recognition techniques have been studied for decades, but there are still many challenges, one of which is the robustness against the variation of view angle. In this paper, the second generation Kinect (Kinect V2) is used as a tool to establish a 3D-skeleton-based gait database, which includes both 3D information of the skeleton joints and the corresponding 2D silhouette images captured by Kinect V2. Based on this dataset, a human walking model is built, and the static and dynamic features are extracted, which are verified to be view-invariant for gait recognition. Referring to the walking model, the gait recognition abilities for the static and dynamic features are investigated respectively and a gait recognition scheme based on the matching-level-fusion of the static and dynamic features is proposed, in which the recognition is achieved by the nearest neighbor classification method. Experiments show that the proposed scheme has robust recognition performance against the variation of view angle.