  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.
