Face recognition using sparse representation with illumination normalization and component features

We merge illumination normalization and component features into the framework of Sparse Representation-based Classification (SRC) for face recognition across illumination. Unlike most SRC-based face recognition which constructs a dictionary from a training set with sufficient illumination variation, the proposed method adopts a dictionary with illumination-normalized training set. This can be the first attempt to show that illumination normalization can upgrade the performance of SRC-based face recognition. To further improve the performance, we add in schemes exploiting local features, and prove its effectiveness. Experiments on FERET and Multi-PIE databases show that the performance of the proposed method can be competitive to the state of the art.