  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.
