Gait classification with different covariate factors

Gait as a biometric has received great attention nowadays as it can offer human identification at a distance without any contact with the feature capturing device. This is motivated by the increasing number of synchronised closed-circuit television (CCTV) cameras which have been installed in many major towns, in order to monitor and prevent crime. This paper proposes a new approach for gait classification with twelve different covariate factors. The proposed approach is consisted of two parts: extraction of human gait features from enhanced human silhouette and classification of the extracted human gait features using fuzzy k-nearest neighbours (KNN). The joint trajectories together with the height, width and crotch height of the human silhouette are collected and used for gait analysis. To improve the recognition rate, two of these features are smoothened before the classification process in order to alleviate the effect of outliers. Experimental results of a dataset involving nine walking subjects have demonstrated the effectiveness of the proposed approach.