Kinect based people identification system using fusion of clustering and classification

The demand of human identification in a non-intrusive manner has risen increasingly in recent years. Several works have already been done in this context using gait-cycle detection from human skeleton data using Microsoft Kinect as a data capture sensor. In this paper we have proposed a novel method for automatic human identification in real time using the fusion of both supervised and unsupervised learning on gait-based features in an efficient way using Dempster-Shafer (DS) theory. Performance comparison of the proposed fusion based algorithm is done with that of the standard supervised or unsupervised algorithm and it needs to be mentioned that the proposed algorithm is able to achieve 71% recognition accuracy.