Faces often appear very small and oriented in surveillance videos because of the need of wide fields of view and typically a large distance between the cameras and the scene. Both low resolution and side-view faces make tasks such as face recognition difficult. As a result, face hallucination or super-resolution techniques of face images are generally needed, which has become a thriving research field. However, most existing methods assume face images have been well aligned into some canonical form (i.e. frontal, symmetric). Therefore, face alignment, especially for low-resolution face images, is a key and first step to the success of many face applications. In this paper, we propose an auto alignment approach for face images at different resolution, which consist of two fundamental steps: 1) To find the locations of facial landmarks or feature points (i.e. eyes, nose, and etc.) even for very low resolution faces; 2) To estimate and correct head poses based on the landmark locations and a 3D reference face model. The effectiveness of this method is shown by the aligned face images and the improved face recognition score on released data sets.