  Whilst recent face-recognition (FR) techniques have made significant progress
on recognising constrained high-resolution web images, the same cannot be said
on natively unconstrained low-resolution images at large scales. In this work,
we examine systematically this under-studied FR problem, and introduce a novel
Complement Super-Resolution and Identity (CSRI) joint deep learning method with
a unified end-to-end network architecture. We further construct a new
large-scale dataset TinyFace of native unconstrained low-resolution face images
from selected public datasets, because none benchmark of this nature exists in
the literature. With extensive experiments we show there is a significant gap
between the reported FR performances on popular benchmarks and the results on
TinyFace, and the advantages of the proposed CSRI over a variety of
state-of-the-art FR and super-resolution deep models on solving this largely
ignored FR scenario. The TinyFace dataset is released publicly at:
https://qmul-tinyface.github.io/.
