Undersampled face recognition based on virtual samples and representation classification

Jun Yang, Yanli Liu

Abstract: Classifiers such as sparse representation or collaborative representation can get good performance in face recognition. But these methods require a number of train samples in each class to construct the dictionary. On the condition of undersampled train samples, their performance decreases dramatically. A novel method is proposed in this paper to address undersampled face recognition problem. Firstly, virtual face images are generated by principal component analysis and mirror transform. Secondly, the test sample is collaboratively represented by the augmented train samples and is recognized by classifier based on representation. A number of face recognition experiments on three benchmark face database show that the recognition accuracy of our method is greater than that of a similar method, while time efficiency of our method is competitive to similar method.