The insufficiency of labeled data is an important problem in image classification such as face recognition. However, unlabeled data are abundant in the real-world application. Therefore, semisupervised learning methods, which corporate a few labeled data and a large number of unlabeled data into learning, have received more and more attention in the field of face recognition. During the past years, graph-based semisupervised learning has been becoming a popular topic in the area of semisupervised learning. In this chapter, we newly present graph-based semisupervised learning method for face recognition. The presented method is based on local and global regression regularization. The local regression regularization has adopted a set of local classification functions to preserve both local discriminative and geometrical information, as well as to reduce the bias of outliers and handle imbalanced data; while the global regression regularization is to preserve the global discriminative information and to calculate the projection matrix for out-of-sample extrapolation. Extensive simulations based on synthetic and real-world datasets verify the effectiveness of the proposed method.
Part of the book: Face Recognition