In this work we focus on learning facial representations that can be adapted
to train effective face recognition models, particularly in the absence of
labels. Firstly, compared with existing labelled face datasets, a vastly larger
magnitude of unlabeled faces exists in the real world. We explore the learning
strategy of these unlabeled facial images through self-supervised pretraining
to transfer generalized face recognition performance. Moreover, motivated by
one recent finding, that is, the face saliency area is critical for face
recognition, in contrast to utilizing random cropped blocks of images for
constructing augmentations in pretraining, we utilize patches localized by
extracted facial landmarks. This enables our method - namely LAndmark-based
Facial Self-supervised learning LAFS), to learn key representation that is more
critical for face recognition. We also incorporate two landmark-specific
augmentations which introduce more diversity of landmark information to further
regularize the learning. With learned landmark-based facial representations, we
further adapt the representation for face recognition with regularization
mitigating variations in landmark positions. Our method achieves significant
improvement over the state-of-the-art on multiple face recognition benchmarks,
especially on more challenging few-shot scenarios.

本文主要研究面部表示学习及其在面部识别中的应用，通过自我监督预训练学习未标记面部图像以提高面部识别性能，并采用基于地标的面部自我监督学习（LAFS）方法，通过提取面部地标实现关键表示学习，同时结合地标特定的数据增强以增加地标信息多样性，进而调节学习过程中地标位置的差异，并在多个面部识别基准测试中取得显著的改进效果，尤其是在更具挑战性的少样本情景中。