A two-stage learning approach to face recognition

Abstract This paper introduces the Collaborative Representation (CR) techniques to small sample size conditions, and propose a Two-Stage learning approach to face recognition based on Collaborative Representation (TSCR). Based on the assumption that the same class samples should lie in the same subspace, we first use the unlabeled samples as dictionary atoms to construct each labeled sample, and obtain the collaborative coefficients by CR. The unlabeled sample with the largest collaborative coefficient is assigned the same class label as the reconstructed labeled sample, and is added to the labeled data set. This process is repeated until about half of the unlabeled samples are labeled and added to the labeled dataset. After that, we employ the original CR approach to classify the left unlabeled samples based on the newly labeled dataset. Experimental results demonstrate that the proposed TSCR is effective on face recognition.

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