A novel Gabor-Kernel face classification method is proposed in this paper. This involves convolving a face image with a series of Gabor kernels at different scales, locations, and orientations to obtain feature vectors. Kernel methods such as Kernel Principal Component Analysis (KPCA) and Kernel Discriminant Analysis (KDA) are then applied to the feature vectors for dimension reduction as well as class separability enhancement. The method has been applied to both face recognition and verification for performance evaluation. Two standard databases: FERET and BANCA database are used for testing. Both results show the robustness of the method: Gabor + KDA against the variance of expression, illumination and pose.