A Model Free Gait Recognition using Random Forest Method

Abstract

This paper presents an approach for model free gait recognition using supervised learning. The method involves utilizing gait entropy images (GEnIs) as features to represent gait information. These GEnI are generated by calculating entropy for individual pixels within silhouettes throughout a full gait cycle. To enhance the robustness of gait recognition, we introduce the inclusion of view angle information, which corresponds to the perspective from which the gait sequence is captured. This additional feature is combined with the GEnI in order to create a comprehensive representation. In scenarios such as real-time CCTV footage, the view angles are frequently unknown. To address this, we propose a two phase algorithm. In the first phase, we predict the view angle of a gait sequence. This prediction is achieved by training a random forest classifier. Once the predicted angle feature is obtained, it is integrated with the GEnI feature. Subsequently, in the second phase, another random forest classifier is trained for gait recognition based on this combined feature set. The capabilty of the proposed approach is assessed through experiments performed on public dataset CASIA-B. Experimental outcomes and comparison with the state-of-the-art provide substantial evidence supporting the effectiveness of our approach. In a cross-walking scenario, we attain accuracies of 95.91% for regular walking, 62.21% for walking while carrying a bag, and 27.64% for walking while wearing a coat.