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A new approach to feature based frontal face recognition with Gabor wavelets and support vector machines is presented in this paper. The feature points are automatically extracted using the local characteristics of each individual face. A kernel that computes the similarity between two feature vectors, is used to map the face features to a space with higher dimension. To find the identity of a test face, the possible labels of each feature vector of that face is found with support vector machines, then the last decision is made by considering all of those labels. By using Gabor features the number of support vectors is reduced compared to directly using the actual image data, and also a better generalization performance is achieved.