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We present a method for performing face recognition based on the fractal neighbor distance (FND). The FND has previously been used for face recognition. What distinguishes our method from others is that we incorporate the use of localized weights with the FND. In a local-to-global feature matching approach, a set of localized weights is used with an algorithm based on the FND that searches for local features. A global score is then derived from each localized score. This set of weights is designed to concentrate around the eyes and nose region of the face, because they contain more discriminating features.