Kernel scatter-difference based discriminant analysis for face recognition

There are two problems with the Fisher linear discriminant analysis (FLDA) for face recognition. One is the singularity problem of the within-class scatter matrix due to small training sample size. The other is that FLDA cannot efficiently describe complex nonlinear variations of face images with illumination, pose and facial expression variations, due to its linear property. A kernel scatter-difference based discriminant analysis is proposed to overcome these two problems. We first use the nonlinear kernel trick to map the input data into an implicit feature space F. Then a scatter-difference based discriminant rule is defined to analysis the data in F. The proposed method can not only produce nonlinear discriminant features in accordance with the principle of maximizing between-class scatter and minimizing within-class scatter, but also avoid the singularity problem of the within class scatter matrix. Experiments on the FERET database show an encouraging recognition performance of the new algorithm.