An Improved Self-Training for Face Recognition

Abstract

Face recognition has attracted considerable concerns in recent years. In practical applications, there are generally a small amount of labeled face images and a lot of unlabeled ones can be available. In this paper, we introduce a semi-supervised face recognition method where semi-supervised LDA (SDA) and Affinity Propagation (AP) are integrated into Self-training. SDA is employed to update the face subspace using labeled and unlabeled face images. And we employ AP to computer the templates which exist in the original face images. A series of experiments on three face datasets are carried out to evaluate the performance of our algorithm. Experimental results illustrate that our algorithm outperforms the other unsupervised, semi-supervised and supervised methods.