Learning auxiliary dictionaries for undersampled face recognition

In this paper, we address the problem of robust face recognition using undersampled data. Given only one or few face images per class, our proposed method not only handles test images with large intra-class variations such as illumination and expression, it is also able to recognize the corrupted ones due to occlusion or disguise. In our work, we advocate the learning of auxiliary dictionaries from the subjects not of interest. With the proposed optimization algorithm which jointly solves the tasks of auxiliary dictionary learning and sparsere-presentation based face recognition, our approach is able to model the above intra-class variations and corruptions for improved recognition. Our experiments on two face image datasets confirm the effectiveness and robustness of our approach, which is shown to outperform state-of-the-art sparse representation based methods.