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This paper proposes a novel face verification algorithm based on multiple feature combination and a support vector machine. The main issue in face verification is to deal with the variability in appearance. It seems difficult to solve this issue using a single feature. Therefore, a combination of mutually complementary features is necessary to cope with various changes in appearance. From this point of view, we describe the feature extraction approaches based on multiple principal component analysis and edge distribution. These features are projected on a new intra-person/extra-person similarity space that consists of several simple similarity measures, and are finally evaluated by a support vector machine supervisor. From the experiments on a realistic and large database, an equal error rate of 0.029 is achieved, which is a sufficiently practical level for many real-world applications.