Semi-supervised Support Vector Learning for Face Recognition

Recently semi-supervised learning has attracted a lot of attention. Different from traditional supervised learning, semi-supervised learning makes use of both labeled and unlabeled data. In face recognition, collecting labeled examples costs human effort, while vast amounts of unlabeled data are often readily available and offer some additional information. In this paper, based on Support Vector Machine (SVM), we introduce a novel semi-supervised learning method for face recognition. The basic idea of the method is that, if two data points are close to each other, they tend to share the same label. Therefore, it is reasonable to search a projection with maximal margin and locality preserving property. We compare our method to standard SVM and transductive SVM. Experimental results show efficiency and effectiveness of our method.