Hyperspectral face recognition based on SLRC for single sample problem

The hyperspectral imaging, adding many dimensions, has practical significance for robust face recognition. However, for hyperspectral face recognition, the main problems are small sample collection, low signal-to-noise ratio and inter band misalignment. In view of these problems, we propose a hyperspectral face recognition method based on SLRC (superposed linear representation classifier) for single sample problem. In the proposed method, one sample from each class is selected as training data, then the rest samples as test data. Since hyperspectral images have multiple bands, we average all bands as prototype dictionaries, and the difference between each band and the corresponding prototype dictionary as variation dictionary. Compared with other sparse representation classification methods, the proposed method can directly use a single sample to train hyperspectral face recognition and has no handcraft feature extraction. Experiments on the hyperspectral face database (PloyU-HSFD) validate that the proposed method can not only greatly increase the accuracy in single sample hyperspectral face recognition, but also improve the computation speed.