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Patch-based face recognition is a recent method which uses the idea of analyzing face images locally, in order to reduce the effects of illumination changes and partial occlusions. Feature fusion and decision fusion are two distinct ways to make use of the extracted local features. Apart from the well-known decision fusion methods, a novel approach for calculating weights for the weighted sum rule is proposed in this paper. Improvements in recognition accuracies are shown and superiority of decision fusion over feature fusion is advocated. In the challenging AR database, we obtain significantly better results using decision fusion as compared to conventional methods and feature fusion methods by using validation accuracy weighting scheme and nearest-neighbor discriminant analysis dimension reduction method.