Reconstruction-based super-resolution has been widely treated in computer vision. However, super-resolution of facial images has received very little attention. Since different parts of a face may have different motions in normal videos, this paper proposes a new method for enhancing the resolution of low-resolution facial image by handling the facial image non-uniformly. We divide low-resolution face image into different regions based on facial features and estimate motions of each of these regions using different motion models. Our experimental results show we can achieve better results than applying super-resolution on the whole face image uniformly.