Person Identification using Skeleton Information from Kinect

In recent past the need for ubiquitous people identification has increased with the proliferation of human- robot interaction systems. In this paper we propose a methodology of recognizing persons from skeleton data using Kinect. First a half gait cycle is detected automatically and then features are calculated on every gait cycle. As part of new features, proposed in this paper, two are related to area of upper and lower body parts and twelve related to the distances between the upper body centroid and the centriods derived from different joints of upper limbs and lower limbs. Feature selection and classification is performed with connectionist system using Adaptive Neural Network (ANN). The recognition accuracy of the individual people using the proposed method is compared with the earlier methods proposed by Arian et. al and Pries et. al. Experimental results indicate that the proposed approach of simultaneous feature selection and classification is having better recognition accuracy compared to the earlier reported ones.