Multi-angle Gait Recognition Based on Skeletal Tracking Data

Abstract

Human gait recognition is one of the most important authentication technologies as it can often happen that people approach a computer system or a robot by walking. Therefore in this study, a multi-angle gait recognition method has been proposed by using skeletal tracking data, measured by an RGB-D camera. The proposed method includes a two stage process, which estimates an optimal gait angle view from the five discrete angles at the first stage and subsequently recognizes human gait based on the specific features for the respective gait angle views. In order to evaluate the proposed method, two types of experiments have been done: gait angle estimation and gait recognition. From the result of the first experiment, the best estimation of 97.4% accuracy has been achieved. In the second experiment, the best gait recognition accuracy was 96.4%. Finally the best gait recognition accuracy with the two stage process has been estimated as 93.9%.