A Semi-supervised 2DPCA Face Recognition Method Based on Self-Training

Abstract

By combining self-training method of the semi-supervised learning with two-dimensional principal component analysis (2DPCA), a semi-supervised learning based face recognition method is proposed. On the basis of two-dimensional principal component analysis, few labeled samples are used to obtain classifier. Then unlabeled samples are classified by the classifier. And according to the self-training method of semi-supervised learning, the face samples with the highest confidence are added to the training set in order to increase the number of face samples in training set. Experimental results on ORL and Yale face database show the effectiveness of the presented method.