  In this paper, we propose a novel gait recognition method based on a
bag-of-words feature representation method. The algorithm is trained, tested
and evaluated on a unique human gait data consisting of 93 individuals who
walked with comfortable pace between two end points during two different
sessions. To evaluate the effectiveness of the proposed model, the results are
compared with the outputs of the classification using extracted features. As it
is presented, the proposed method results in significant improvement accuracy
compared to using common statistical features, in all the used classifiers.
