Local and non-local graph regularized sparse coding for face recognition

The recent emerging sparse coding (SC) algorithms do not take local manifold structure of samples into consideration, while graph regularized sparse coding (GraphSC) algorithm only constrains the locality consistency of samples. Furthermore, the graph construction approach based on k-nearest-neighbor usually pre-defines the number of neighbors for all the samples, which may fails to fit the intrinsic structure of each sample. To address these issues, we propose an local and nonlocal graph regularized sparse coding (LN-GraphSC) algorithm. LN-GraphSC incorporates both local and nonlocal information of samples at the same time. On the other hand, to alleviate the problem of neighbor parameter selection, we use average distance of each sample to wisely determine its own local and nonlocal samples. To verify the effectiveness of our proposed method, we evaluate our method on the task of face recognition. The experimental results on ORL and Yale face databases show our method has competitive performance when compared to SC and GraphSC.