  Recognition in low quality face datasets is challenging because facial
attributes are obscured and degraded. Advances in margin-based loss functions
have resulted in enhanced discriminability of faces in the embedding space.
Further, previous studies have studied the effect of adaptive losses to assign
more importance to misclassified (hard) examples. In this work, we introduce
another aspect of adaptiveness in the loss function, namely the image quality.
We argue that the strategy to emphasize misclassified samples should be
adjusted according to their image quality. Specifically, the relative
importance of easy or hard samples should be based on the sample's image
quality. We propose a new loss function that emphasizes samples of different
difficulties based on their image quality. Our method achieves this in the form
of an adaptive margin function by approximating the image quality with feature
norms. Extensive experiments show that our method, AdaFace, improves the face
recognition performance over the state-of-the-art (SoTA) on four datasets
(IJB-B, IJB-C, IJB-S and TinyFace). Code and models are released in
https://github.com/mk-minchul/AdaFace.
