A Novel Gait Recognition Method Via Fusing Shape and Kinematics Features

Abstract

Existing methods of gait recognition are mostly based on either holistic shape information or kinematics features. Both of them are very important cues in human gait recognition. In this paper we propose a novel method via fusing shape and motion features. Firstly, the binary silhouette of a walking person is detected from each frame of the monocular image sequences. Then the static shape is represented using the ratio of the body’s height to width and the pixel number of silhouette. Meanwhile, a 2D stick figure model and trajectory-based kinematics features are extracted from the image sequences for describing and analyzing the gait motion. Next, we discuss two fusion strategies relevant to the above mentioned feature sets: feature level fusion and decision level fusion. Finally, a similarity measurement based on the gait cycles and two different classifiers (Nearest Neighbor and KNN) are carried out to recognize different subjects. Experimental results on UCSD and CMU databases demonstrate the feasibility of the proposed algorithm and show that fusion can be an effective strategy to improve the recognition performance.