A symmetric transformation for LDA-based face verification

One of the major problems in face verification is to deal with a few numbers of images per person to train the system. A solution to that problem is to generate virtual samples from a unique image by doing simple geometric transformations such as translation, scale, rotation and vertical mirroring. In this paper, we propose to use a symmetric transformation to generate a new virtual sample. This symmetric virtual sample is obtained by computing the average between the original image and the vertical mirrored image. The face verification system is based on LDA feature extraction, successfully used in previous studies, and MLP for classification. Experiments were carried out on a difficult multi-modal database, namely BANCA. Results on this database show that our face verification system performs better that the state-of-the-art and also that the addition of the symmetric virtual sample improves the performance.