How to improve the resolution of face images is a classic problem in computer vision. During video surveillance, since the target person is faraway from the camera, the result is often a low-resolution face image. Aiming at this problem, we propose a face super-resolution restoration algorithm combining principal component analysis (PCA) and maximum a posteriori probability (MAP). Firstly, we get the characteristics of the high-resolution face database based on the PCA model. Secondly, we calculate the representation coefficients of the input low-resolution face images on these features by MAP and reconstruct the corresponding high-resolution features. Thirdly, we make the constraint enhancement of the reconstructed features. Finally, we obtain the final super-resolution restoration images based on the average vector of high-resolution face database. In order to verify the effectiveness of this algorithm, we make the experiments that the images in the AR face database are amplified four times using this method and other methods. The result of this method is superior to other methods in either visual effects or evaluation indicators. This algorithm not only improves the resolution of face images, but also maintains the edge information of the image better.