  The performance of face recognition system degrades when the variability of
the acquired faces increases. Prior work alleviates this issue by either
monitoring the face quality in pre-processing or predicting the data
uncertainty along with the face feature. This paper proposes MagFace, a
category of losses that learn a universal feature embedding whose magnitude can
measure the quality of the given face. Under the new loss, it can be proven
that the magnitude of the feature embedding monotonically increases if the
subject is more likely to be recognized. In addition, MagFace introduces an
adaptive mechanism to learn a wellstructured within-class feature distributions
by pulling easy samples to class centers while pushing hard samples away. This
prevents models from overfitting on noisy low-quality samples and improves face
recognition in the wild. Extensive experiments conducted on face recognition,
quality assessments as well as clustering demonstrate its superiority over
state-of-the-arts. The code is available at
https://github.com/IrvingMeng/MagFace.
