Low-Resolution Face Recognition via Sparse Representation of Patches

Images resolution plays an important role during face recognition. Low-resolution face images will reduce drastically the performance of face recognition algorithms. In this paper, we propose a novel approach for low-resolution face recognition. Our method first exacts patches with different size from the face images. Each patch is represented by its LBP feature. Then, we find the sparse representation of these patches based on corresponding LBP features of high-resolution face image patches. At last, we use AdaBoost to select the most discriminative patches, each of which is treated as a weak classifier, and make the ensemble of these patches weak classifiers for final decision. Experiments on Extended Yale B face database showed our method achieved high performance for low-resolution face recognition.