Coupled non-negative matrix factorization for low-resolution face recognition

The existing methods based on coupled mapping for low-resolution face recognition (LRFR) only map images with different dimensions to the same dimension, the mapping process and the mapped images have no clear physical meaning. In the human mind, the whole could be regarded as a combination of its different local features. Therefore, face images can also be regarded as the composition of different local features. For the face images of the same target with different resolutions, their local features are different in scale, but the way of forming the whole by local features is consistent. Based on this idea, a novel coupled non-negative matrix factorization algorithm (CNMF) algorithm is proposed to deal with the LRFR problem. In the learning process of the proposed method, the high- and low-resolution images are expressed as linear combination of local features respectively. The representation coefficients of different resolution images of the same target are kept coupled to obtain the respective basis matrix. The proposed CNMF is more interpretable in extracting common features of different dimension data. The experimental results show that the proposed coupled non-negative matrix factorization method is superior to the other state-of-the-art low-resolution image recognition methods.