Sparse representation with dense matching for face recognition from single sample per person

Many face recognition tasks encounter the problem of having only one sample for each subject, which is known as the single sample per person (SSPP) problem. To tackle the problem, we propose a strategy of sparse representation with dense matching method. First, an external training set is used to form an intra-class variation dictionary. Then, noting that captured facial features will vary with facial expression and pose, dense matching is applied to preprocess facial patches. This offers a courtesy way to overcome this obstacle and improves coherence of facial features. Finally, we make decision by extended sparse representation based classification (ESRC) based on the learned dictionary. Experiments on AR, Extended Y ale B, CMU PIE and FERET datasets demonstrate that our proposed method outperforms many state-of-the-art methods on face recognition with SSPP.