Improving single view gait recognition using sparse representation based classification

This paper explores a better way of recognition using sparse based representation, by taking into account a number of covariates that affect single view based gait. Nevertheless, the conventional methods couldn't handle covariates effectively. Our propose framework comprises a dictionary, which describes five segments of a subject over a gait period. The feature vectors are educed from ellipse based parameters from each segments and fused to form a covariance matrix. Each matrix is used as dictionary atom and solved using — l1 — minimization. The linear representations of sparse codes of different atoms are used for recognition. The proposed method is compared with that of state-of-the-art methods