TR2018-140

Super-resolution of Very Low-Resolution Faces from Videos



Faces appear in low-resolution video sequences in various domains such as surveillance. The information accumulated over multiple frames can help super-resolution for high magnification factors. We present a method to super-resolve a face image using the consecutive frames of the face in the same sequence. Our method is based on a novel multi-input-single-output framework with a Siamese deep network architecture that fuses multiple frames into a single face image. Contrary to existing work on video super-resolution, it is model free and does not depend on facial landmark detection that might be difficult to handle for very low-resolution faces. The experiments show that the use of multiple frames as input improves the performance compared to single-inputsingle-output
systems.