Global and Local Information Based Spherical Marginal Fisher Analysis for Face Recognition

We proposed a new face recognition algorithm, termed Spherical Marginal Fisher Analysis (SMFA). Different from traditional Marginal Fisher Analysis (MFA) in which we don’t select a certain number of nearest samples between different classes, but contain all the needed samples in some content. Meanwhile, we add the information between sample centers as applied in Linear Discriminant Analysis (LDA). Experimental results on the ORL and Yale face databases show our method outperforms other linear methods.