Face recognition based on Low-Rank matrix Representation

Based on the recent success of Low-Rank matrix Representation (LRR), we propose a novel classification method for robust face recognition, named LRR-based Classification (LRRC). By the ideal that if each data class is linearly spanned by a subspace of unknown dimensions and the data are noiseless, the lowest-rank representations of a set of test vector samples with respect to a set of training vector samples have the nature of being both dense for within-class affinity and almost zero for between-class affinities. Consequently, the LRR exactly reveals the classification of the data. Our experimental results demonstrate that LRRC has competitive with state-of-the-art classification methods.