Multi-view gait recognition based on point cloud gallery sets

Abstract

As a biometric recognition technology with the advantages of long-distance and anti-camouflage, gait recognition has great application value in public security criminal investigation, security monitoring and other fields. Due to the influence of external factors such as viewing angle, clothing and light on the appearance of gait sequence, most of the current gait recognition algorithms have low accuracy and cannot be applied on a large scale. Aiming at the view angle problem in gait recognition, we proposed a method of constructing full-view gait gallery set based on dual Kinect cameras and edge point cloud registration. Then, we used the RGB camera to collect gait sequence from any angle of view (including depression angle), and designed a gait feature matching network called GaitCrossNet, which is used to match the RGB gait contour sequence to the sequence projected from point cloud sets. The experimental results showed that the average recognition accuracy of the algorithm proposed in this paper can reach 85.2%, which has obvious advantages compared with the existing recognition schemes.