Singular value decomposition-based virtual representation for face recognition

Abstract

In the field of face recognition, a key issue is whether there are a sufficient number of face training samples with valid information. Due to the complexity of human face images, face recognition is easy to be affected by the external environment such as light intensity, gesture expression, hairstyle, and occlusion. Therefore, it is difficult to obtain enough effective samples in practical applications. In this paper, we propose a new algorithm that generates virtual images by utilizing the information of the test sample via singular value decomposition. The virtual images not only extend the training sample set but also can better adapt to the test sample. In addition, we use the weighted score fusion scheme to calculate the ultimate result, which can better take advantages of data from different sources including original images and virtual images. Experimental results on the Extended Yale_B, AR, GT, ORL, and FERET face databases prove that our algorithm can obtain satisfactory performance.