Person Identification in Natural Static Postures Using Kinect

Abstract

Automatic person identification using un-obtrusive methods are of immense importance in the area of computer vision. Anthropometric approaches are robust to external factors including environmental illumination and obstructions due to hair, spectacles, hats or any other wearable. Recently, there have been efforts made on people identification using walking pattern of the skeleton data obtained from Kinect. In this paper we investigate the possibility of identification using static postures namely sitting and standing. Existing gait based identifications, mostly rely on the dynamics of the joints of the skeleton data. In case of static postures the motion information is not available, hence the identification mainly relies on the static distance information between the joints. Moreover, the variation of pose in a particular posture makes the identification more challenging. The proposed methodology, initially sub-divides the body-parts into static, dynamic and noisy parts followed by a combinatorial element responsible for selectively extracting features for each of those parts. Finally a radial basis function support vector machine classifier is used to perform the training and testing for the identification. Results indicate an identification accuracy of more than 97 % in terms of F-score for 10 people using a dataset created with various poses of natural sitting and standing posture.