Supervised maximum variance unfolding and its application to gait recognition
监督最大差伸展算法及其在步态识别中的应用

There are a number of covariate factors that affect recognition performance. In order to improve the gait recognition rate, based on maximum variance unfolding algorithm, this paper proposed a supervised MVU method and applied it to gait recognition. The proposed method could seek an optimal subspace where samples in different submanifolds were located further and samples in the same submanifolds were clustered closer. The experimental results on real-world gait image databases show the effectiveness and feasible of the proposed method.