A novel Gabor-Kernel face recognition method is proposed in this paper. This involves convolving a face image with a series of Gabor wavelets at different scales, locations, and orientations. 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. A database of 600 frontal-view face images from the FERET face database is used to test the method. Experimental results demonstrate the advantage of Kernel methods over classical Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Significant improvements are also observed when the Gabor filtered images are used for feature extraction instead of the original images. The Gabor + KDA method achieves 92% recognition accuracy using only 35 features of a face image.