Human Identification by Gait Using Corner Points

Recently human gait has become a promising and very important biometric for identification. Current research on gait recognition is usually based on an average gait image or a silhouette sequence, or a motion structure model. In this paper, the information about gait is obtained from the disparity on time and space of the different parts of the silhouette. This paper proposes a gait recognition method using edge detection, identification of corner points from edges, and selection of control points out of those corner points. Here, the images of moving human figures are subtracted from background by simple background modeling technique to obtain binary silhouettes. A gait signature of a person is taken as silhouette images of a complete gait cycle. A complete gait cycle is then divided into different frames in such a way that the information of the person’s gait style can be represented fully. One given unknown gait cycle is compared with stored gait cycles in terms of a cyclic distances between control points of an image of input gait cycle with that of corresponding image of the stored gait cycle. Experimental results show that our method is encouraging in terms of recognition accuracy.