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The use of interest point detectors and SIFT descriptors for face recognition is studied in this paper. There are two main novelties with respect to previous approaches using SIFT features. First, the use of two scale-invariant interest point detectors (namely, Harris-Laplace and difference of Gaussians) which are combined in order to detect both corner-like structures and blob-like structures in face images. Second, the distance measure used, which takes into account both the number of matching points found between two images (according to their SIFT descriptors) and the coherence of these matches in terms of scales, orientations and spacial configuration. The results obtained with our model-based algorithm are compared with those of a classic appearance-based face recognition method (PCA) over two different face databases: the well-known AT&T database and a face database created at our university.