This paper presents a gait recognition method which is based on spatio-temporal movement characteristics of human subject with respect to surveillance camera. Different measures, like leg rise from ground (LRFG), the angles created between the legs with the centre of Mass (ABLC), angles created between the Centre of Mass Knee and Ankle with the (CKA), angles created between Centre of Mass, Wrist and knee (CWK), the distances between the control points and centre of Mass (DCC) have been taken as different features. Fourier descriptor has been used for shape extraction of individual frames of a subject. Statistical approach has been used for recognition of individuals based on the n feature vectors, each of size 23(collected from LRFG, ABLCs, CKA, CWK and DCCs) for each video frame. It has been found that recognition result of our approach is encouraging with compared to other recent methods.

In this work, a simple characterization of human gait, which can be used for surveillance purpose, is presented. Different measures, like leg rise from ground (LRFG), the angles created between the legs with the centroid (ABLC), the distances between the control points and centroid (DBCC) have been taken as different features. In this method, the corner points from the edge of the object in the image have been considered. Out of several corner points thus extracted, a set of eleven significant points, termed as control points, that effectively and rightly characterize the gait pattern, have been selected. The boundary of the object has been considered and using control points on the boundary the centroid of those has been found out. Statistical approach has been used for recognition of individuals based on the n feature vectors, each of size 23(collected from LRFG, ABLCs, and DBCCs) for each video frame, where n is the number of video frames in each gait cycles. It has been found that recognition result of our approach is encouraging with compared to other recent methods.

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.