Deep multi-frame face super-resolution
This addresses the challenge of face verification and recognition from small images, which is important for security and surveillance applications, but it is incremental as it builds on existing multi-frame and super-resolution ideas.
The paper tackled the problem of face recognition from low-resolution images by developing a holistic system for multi-frame face super-resolution, alignment, and recognition, showing a notable improvement in identification score on the YouTube Faces dataset compared to baselines including single-image super-resolution.
Face verification and recognition problems have seen rapid progress in recent years, however recognition from small size images remains a challenging task that is inherently intertwined with the task of face super-resolution. Tackling this problem using multiple frames is an attractive idea, yet requires solving the alignment problem that is also challenging for low-resolution faces. Here we present a holistic system for multi-frame recognition, alignment, and superresolution of faces. Our neural network architecture restores the central frame of each input sequence additionally taking into account a number of adjacent frames and making use of sub-pixel movements. We present our results using the popular dataset for video face recognition (YouTube Faces). We show a notable improvement of identification score compared to several baselines including the one based on single-image super-resolution.