Global-local fisher discriminant approach for face recognition

In this paper, we proposed a linear discriminant approach, namely global-local Fisher discriminant analysis (GLFDA) that explicitly considers both the local and global discriminant structures embedded in data. To be specific, GLFDA constructs two graphs to, respectively, model the global and local discriminant structures and then incorporates discriminant structures and local intrinsic structure, which characterizes the within-class compactness, into the objective function for dimensionality reduction. Thus, GLFDA well encodes the discriminant information, especially the local discriminant information of data. Experimental results on AR, YALE, and UMIST databases show the effectiveness of the proposed algorithm.