Set-to-set gait recognition across varying views and walking conditions

This paper examines the multiview gait recognition problem in which human gait sequences are collected from several different views simultaneously. Motivated by the fact that set-based feature representation can handle certain intra-subject variations, we propose a new Multiview Subspace Representation (MSR) method for gait recognition across varying views and walking conditions. It takes samples collected from different views of the same subject as a feature set and uses a subspace to represent such information. Then, the similarity of two subjects is measured by the distance between two subspaces and a simple yet effective Weighted Subspace Distance (WSD) algorithm is applied to calculate the similarity. There are two notable advantages of our proposed method: 1) we need not know the exact view of the test gait sequence in advance, and 2) some extent of intra-subject variations can be effectively handled. Experimental results on two benchmark multi-view gait databases are presented to demonstrate the effectiveness of the proposed method.