In this paper, we tackle the problem of gait recognition based on the model-free approach. Numerous methods exist; they all lead to high dimensional feature spaces. To address the problem of high dimensional feature space, we propose the use of the Random Forest algorithm to rank features' importance. In order to efficiently search throughout subspaces, we apply a backward feature elimination search strategy. Our first experiments are carried out on unknown covariate conditions. Our first results suggest that the selected features contribute to increase the CCR of different existing classification methods. Secondary experiments are performed on unknown covariate conditions and viewpoints. Inspired by the location of our first experiments' features, we proposed a simple mask. Experimental results demonstrate that the proposed mask gives satisfactory results for all angles of the probe and consequently is not view specific. We also show that our mask performs well when an uncooperative experimental setup is considered as compared to the state-of-the art methods. As a consequence, we propose a panoramic gait recognition framework on unknown covariate conditions. Our results suggest that panoramic gait recognition can be performed under unknown covariate conditions. Our approach can greatly reduce the complexity of the classification problem while achieving fair correct classification rates when gait is captured with unknown conditions. 
Feature subset selection applied to model-free gait recognition