Joint Subspace Learning for View-Invariant Gait Recognition

We propose in this paper a novel joint subspace learning (JSL) method for view-invariant gait recognition. Inspired by the finding that if a 3-D object can be well represented by the weighted sum of a sufficiently small number of prototypes in the same view, then the representation coefficients are generally consistent across different views, we propose to use these coefficients as view-invariant features for gait recognition. Firstly, we conduct JSL to obtain the prototypes of different views. Then, we represent each sample in both the gallery set and probe set acquired from different views as a linear combination of these prototypes in the corresponding views, and extract the coefficients for feature representation. Lastly, we perform recognition by using a simple nearest neighbor rule. Experimental results on the widely used CASIA-B gait database demonstrate the effectiveness of the proposed method.