Kernel-based optimized feature vectors selection and discriminant analysis for face recognition

In practice, face image data distribution is very complex because of pose, illumination and facial expression variation, so it is inadequate to describe it just by Fisherface or Fisher linear discriminant analysis (FLDA). In the paper a method is presented for face recognition using kernel-based optimized feature vectors selection and discriminant analysis. The kernel trick is used to select an optimized subset from the data and form a subspace into the feature space that can capture the structure of the entire data into the feature space according to geometric consideration. Then all the data are projected into this subspace and FLDA is performed in this subspace to extract nonlinear discriminant features of the data for face recognition. Another similar analysis method is kernel-based Fisher discriminant analysis (KFDA), which transforms all the data into the feature space and FLDA is performed in the feature space. The proposed method is compared with Fisherface and KFDA on two benchmarks, and experimental results demonstrate that it outperforms Fisherface and can give the same recognition accuracy as KFDA, but its computational complexity is reduced against KFDA.