A supervised NMF algorithm to enhance the classification accuracy of the NMF algorithm is presented. The method employs discriminant analysis in the features derived from NMF. In this way, intrasubject variation is minimized, while the intersubject variation is maximized feature extraction procedure. Experimental results on public available face databases show that the proposed method has higher recognition rate than NMF and other subspace methods.