Abstract
Despite considerable advances made in face recognition in recent years, the recognition performance still suffers from insufficient training samples. Hence, various algorithms have been proposed for addressing the problems of small sample size with dramatic variations in illuminations, poses and facial expressions in face recognition. Among these algorithms, the virtual sample generation technology achieves promising performance with reasonable and effective mathematical function and easy implementation. In this paper, we systematically summarize the research progress in the virtual sample generation technology for face recognition and categorize the existing methods into three groups, namely, (1) construction of virtual face images based on the face structure; (2) construction of virtual face images based on the idea of perturbation and distribution function of samples; (3) construction of virtual face images based on the sample viewpoint. We carry out thorough and comprehensive comparative study in which different methods are compared by conducting an in-depth analysis on them. It demonstrates the significant advantage of combining the virtual sample generation technology with representation based methods.
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