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We propose a novel classification method, called nearest intra-class space (NICS), for face recognition. In our method, the distribution of face patterns of each person is represented by the intra-class space to capture all intra-class variations. Then, a regular principal subspace is derived from each intra-class space using principal component analysis. The classification is based on the nearest weighted distance, combining distance-from-subspace and distance-in-subspace, between the query face and each intra-class subspace. Experimental results show that the NICS classifier outperforms other classifiers in terms of recognition performance.