Improved Gait Classification with Different Smoothing Techniques

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 by identifying the criminal or suspect. This paper present a method to improve gait classification results by applying smoothing techniques on the extracted gait features. The proposed approach is consisted of three parts: extraction of human gait features from enhanced human silhouette, smoothing process on extracted gait features and classification by fuzzy k-nearest neighbours (KNN). The extracted gait features are height, width, crotch height, step-size of the human silhouette and joint trajectories. To improve the recognition rate, two of these extracted gait features are smoothened before the classification process in order to alleviate the effect of outliers. The proposed approach has been applied on a dataset of nine subjects walking bidirectionally on an indoor pathway with twelve different covariate factors. From the experimental results, it can be concluded that the proposed approach is effective in gait classification.