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A matching pursuit based similarity measure for face recognition

Sparse representation can not only uncover the primary or meaningful semantic information of a sample, but also have some advantages, such as simple, flexible and so on. Compared with other sparse representation algorithms, matching pursuit based on greedy iterative algorithm is more effective, this article takes it to select neighbors. All the training samples are used to build the overcomplete dictionary, we want to find the most relevant samples serve as a close neighbor of the sample. Next a new concept called similarity measure is proposed. We determine the weight of the neighbor matrix by comparing three factors: the ordered list of dictionary elements, the set of coefficients and the residue produced from matching pursuits approximation, finally it gets the optimal projection subspace by minimizing the Objective function. Compared with the other feature extraction method, the proposed method have a better recognition impact and more robust. The AR and FERET face image database show that it is effective.