Coupling Face Registration and Super-Resolution

Existing approaches to learning-based face image super-resolution require low-resolution testing inputs manually registered to pre-aligned highresolution training models [9, 12, 13, 5]. This restricts automatic applications to live images and video. In this paper, we propose a multi-resolution patch tensor based model to automatically super-resolve and register low-resolution testing face images. Face candidates are triggered first by a face detector giving the subwindows with their coarse initial positions and scales in a large image frame. This initialises a combined registration and super-resolution process. Rather than manually aligning each coarsely detected face subwindow to some predefined template, based on its position and scale, we scan all the potential face subwindows across different positions and scales, and obtain registration and super-resolution in a simultaneous process. The superresolution result which is optimally correlated to its original low-resolution face subwindow is also guaranteed to be the best super-resolved reconstruction. We verify our approach by experimenting on MIT+CMU face detection dataset, the promising results demonstrate the robustness of our approach on learning-based face super-resolution on real images.