Single sample face identification utilizing sparse discriminative multi manifold embedding

This paper describes three methods to improve single sample dataset face identification. The recent approaches to address this issue use intensity and do not guarantee for the high accuracy under uncontrolled conditions. This research presents an approach based on Sparse Discriminative Multi Manifold Embedding (SDMME), which uses feature extraction rather than intensity and normalization for pre-processing to reduce the effects of uncontrolled condition such as illumination. In the worst case of illumination this study improves identification accuracy about 17% compare to current methods.