Nuclear norm-based two-dimensional discriminant locality preserving projection for face recognition

Two-dimensional discriminant locality preserving projection (2DDLPP) is an effective method for image feature extraction. However, original 2DDLPP is based solely on the Euclidean distance, which is sensitive to noises and illumination changes in images. To overcome this drawback, we propose a method named nuclear norm-based two-dimensional discriminant locality preserving projection (NN2DDLPP). In NN2DDLPP, two optimal neighbor graphs are first built. Then the nuclear norm-based between-class scatter and within-class scatter are defined. Finally, in order to obtain an optimal projection matrix, the ratio of between-class scatter to within-class scatter is maximized. Using nuclear norm metric and labeled information, NN2DDLPP can both efficiently extract the discriminative features and improve the robustness to illumination changes and noises. Experiments carried out on several different face image databases validate that NN2DDLPP is efficacious for face recognition and better than other related works.