Pose Based Person Identification Using Kinect

The importance of automatic person identification using non-intrusive biometric modality has created enormous interest in computer vision society over the last few years. For this, gait based person recognition is receiving much more attention in different applications like visual surveillance, security control, people counting. In this paper, we have presented a gait based person identification system using 3D human pose modeling for any arbitrary walking pattern in any unrestricted indoor environment, using Microsoft Kinect sensor. Instead of estimating gait cycle, we have modeled the gait pattern with a spatiotemporal set of key poses and sub-poses which occur periodically in different gait cycles. The robustness of the solution is increased by outlier detection to handle noisy skeleton data obtained from Kinect. The performance of the proposed system is also assessed with rotating Kinect setup to increase the field of view of single Kinect. We have done the average and worst case performance evaluation of the system with respect to the existing Kinect based approaches. It needs to be mentioned that our proposed person identification system is able to achieve a frame level F-score of more than 90% for 20 subjects with fixed Kinect setup.