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, and
pre-trained models to facilitate future research.

本文讨论了学习通用人脸表征的最佳方法，介绍了用于面部表征学习的综合评估基准，研究了人脸表示学习的两种方法（监督和非监督预训练），并调查了训练数据集的重要特征，通过大量实验，发现无监督的预训练方法在各种人脸任务中表现出一致性和显著的准确性提高，同时也发现现有的一些人脸视频数据集存在大量的冗余。