  What is the best way to learn a universal face representation? Recent work on
Deep Learning in the area of face analysis has focused on supervised learning
for specific tasks of interest (e.g. face recognition, facial landmark
localization etc.) but has overlooked the overarching question of how to find a
facial representation that can be readily adapted to several facial analysis
tasks and datasets. To this end, we make the following 4 contributions: (a) we
introduce, for the first time, a comprehensive evaluation benchmark for facial
representation learning consisting of 5 important face analysis tasks. (b) We
systematically investigate two ways of large-scale representation learning
applied to faces: supervised and unsupervised pre-training. Importantly, we
focus our evaluations on the case of few-shot facial learning. (c) We
investigate important properties of the training datasets including their size
and quality (labelled, unlabelled or even uncurated). (d) To draw our
conclusions, we conducted a very large number of experiments. Our main two
findings are: (1) Unsupervised pre-training on completely in-the-wild,
uncurated data provides consistent and, in some cases, significant accuracy
improvements for all facial tasks considered. (2) Many existing facial video
datasets seem to have a large amount of redundancy. We will release code,
pre-trained models and data to facilitate future research.
