Low-resolution Face Recognition with Variable Illumination Based on Differential Images

Low resolution severely degrades the detail of face images, while bad illumination condition further increases the difficulty of face recognition. In this paper, we present a new differential images based face recognition approach to deal with low resolution and variable illumination problems. Firstly, we resort to down-sampling and interpolation to build differential image database. Then feature extraction is preformed to project the differential images into differential feature subspace. For recognizing input low-resolution (LR) face images that suffer from illumination variation, normalization are employed to compensate the illumination of LR images and hallucination method is to infer the corresponding project coefficients in the differential feature subspace. Finally, similarity measure is used for face discrimination. Different from Eigenspace-based face recognition techniques, the utilization of differential images instead of original images ensures the distinction between face images of different subjects more effectively. Experiments demonstrate that the proposed approach can improve recognition rate of LR face under variable illumination significantly.