A new face recognition method based on SVD perturbation for single example image per person

At present, there are many methods for frontal view face recognition. However, few of them can work well when only one example image per class is available. In this paper, we present a new method based on SVD perturbation to deal with the `one example image' problem and two generalized eigenface algorithms are proposed. In the first algorithm, the original image is linearly combined with its derived image gotten by perturbing the image matrix's singular values, and then principal component analysis (PCA) is performed on the joined images. In the second algorithm, the derived images are regarded as independent images that could augment training image set, and then PCA is performed on all the training images available, including the original ones and the derived ones. The proposed algorithms are compared with both the standard eigenface algorithm and the (PC)^2A algorithm which is proposed for addressing the `one example image' problem, on the well-known FERET database with three different image resolutions. Experimental results show that the generalized eigenface algorithms are more accurate and use far fewer eigenfaces than both the standard eigenface algorithm and the (PC)^2A algorithm.