Face recognition using FLDA with single training image per person

Fisher linear discriminant analysis (FLDA) has been widely used for feature extraction in face recognition. However, it cannot be used when each object has only one training sample because the intra-class variations cannot be statistically measured in this case. In this paper, a novel method is proposed to solve this problem by evaluating the within-class scatter matrix from the available single training image. By using singular value decomposition (SVD), we decompose the face image into two complementary parts: a smooth general appearance image and a difference image. The later is used to approximately evaluate the within-class scatter matrix and thus the FLDA can be applied to extract the discriminant face features. Experimental results show that the proposed method is efficient and it can achieve higher recognition accuracy than many existing schemes.