Face Recognition with Kernel Correlation Filters on a Large Scale Database

Recently, Direct Linear Discriminant Analysis (D-LDA) and Gram-Schmidt LDA methods have been proposed for face recognition. By also utilizing some of the null-space of the within-class scatter matrix, they exhibit better performance compared to Fisherfaces and Eigenfaces. However, these linear subspace methods may not discriminate faces well due to large nonlinear distortions in the face images. Redundant class dependence feature analysis (CFA) method exhibits superior performance compared to other methods by representing nonlinear features well. We show that with a proper choice of kernel parameters used with the proposed Kernel Correlation Filters within the CFA framework, the overall face recognition performance is significantly improved. We present results of this proposed approach on a large scale database from the Face Recognition Grand Challenge (FRGC) which contains over 36,000 images.