How can we reconstruct facial image from partially occluded or low-resolution one?

This paper presents our method for reconstructing facial image from a partially occluded facial image or a low-resolution one using example-based learning. Faces are modeled by linear combinations of prototypes of shape and texture. With the shape and texture information from an input facial image, we can estimate optimal coefficients for linear combinations of prototypes of shape and texture by simple projection for least square minimization. The encouraging results of the proposed method show that our method can be used to improve the performance of the face recognition by reconstructing facial image from a partially occluded facial image or a low-resolution one.