Selecting the Effective Regions for Gait Recognition by Sparse Representation

In gait recognition the variations of clothing and carrying conditions can change the human body shape greatly. So the gait feature extracted from human body images will be greatly affected and the performance will decrease drastically. Thus in this paper, we proposed one gait recognition method to improve the robustness towards these variations. The main idea is to select effective regions by sparse representation. If the region can be represented by features from gait data without variations, that means the region is not occluded by some objects. Experimental results on a large gait dataset show that the proposed method can achieve high recognition rates, and even outperform some deep learning based methods.