On the selection of spatiotemporal filtering with classifier ensemble method for effective gait recognition

In this paper, we improve the performance of gait recognition by modeling human’s motion with spatiotemporal gait features. Since existing methods often use average of silhouettes, i.e., gait energy image to model the gait, temporal information of walking may not be preserved under covariate factors. To handle such features in different conditions, we study the gait model from energy viewpoint. In the proposed method, energy of a gait, i.e., spatiotemporal feature, is derived from a newly designed filtering approach and the energies within a period will be aggregated into a single template that is called gait spatiotemporal image. The required features are truly extracted from spatial and temporal impulse responses that are redesigned and optimized for the gait. Moreover, to recognize the gait under covariate factors, a hybrid decision-level classifier based on random subspace method has been utilized for the given templates. Experimental results on well-known public datasets demonstrate the efficacy of our model. The proposed gait recognition system achieves the recognition rate of 72.25% for Rank1 and 85.64% for Rank5 on the USF dataset that is improved by at least 2% in Rank1 and 0.3% in Rank5 with respect to recent template-based methods.