Robust Face Super-Resolution Using Free-Form Deformations for Low-Quality Surveillance Video

Abstract

Recently, the demand for face recognition to identify persons from surveillance video cameras has rapidly increased. Since surveillance cameras are usually placed at positions far from a person's face, the quality of face images captured by the cameras tends to be low. This degrades the recognition accuracy. Therefore, aiming to improve the accuracy of the low-resolution-face recognition, we propose a video-based super-resolution method. The proposed method can generate a high-resolution face image from low-resolution video frames including non-rigid deformations caused by changes of face poses and expressions without using any positional information of facial feature points. Most existing techniques use the facial feature points for image alignment between the video frames. However, it is difficult to obtain the accurate positions of the feature points from low-resolution face images. To achieve the alignment, the proposed method uses a free-form deformation method that flexibly aligns each local region between the images. This enables super-resolution of face images from low-resolution videos. Experimental results demonstrated that the proposed method improved the performance of super-resolution for actual videos in terms of both image quality and face recognition accuracy.