Face recognition based on KPCA and SVM

KPCA algorithm can solve the problem of nonlinear characteristic that the PCA algorithm can´t handle with and the traditional curvelet decomposition algorithm cannot take full advantage of the fine scale component information. So we put forward KPCA algorithm and data fusion algorithm. The KPCA algorithm has a good effect on extracting face contour and the curve detail information through internal nonlinear kernel function. Data fusion algorithm can make use of different scale of image which decomposed by curvelet according to certain proportion. Support Vector Machine (SVM) has the strong ability of classification of small samples and the advantage of dealing with nonlinear and high dimension. In this paper, the KPCA and SVM methods were combined with and used for face recognition. At first, the paper made use of the low-frequency of the face images decomposed by curvelet transform, then the feature vectors were extracted by KPCA, and the strategy of “one vs one” of SVM was chosen to perform recognition. The results based on the ORL and Yale shows the success of KPCA+SVM employed in face recognition. Then the curvelet faces were reduced dimension by PCA. The coarse information and the fine information were combined by data fusion. The results based on the ORL shows the success of data fusion employed in face recognition.