  Recently, a popular line of research in face recognition is adopting margins
in the well-established softmax loss function to maximize class separability.
In this paper, we first introduce an Additive Angular Margin Loss (ArcFace),
which not only has a clear geometric interpretation but also significantly
enhances the discriminative power. Since ArcFace is susceptible to the massive
label noise, we further propose sub-center ArcFace, in which each class
contains $K$ sub-centers and training samples only need to be close to any of
the $K$ positive sub-centers. Sub-center ArcFace encourages one dominant
sub-class that contains the majority of clean faces and non-dominant
sub-classes that include hard or noisy faces. Based on this self-propelled
isolation, we boost the performance through automatically purifying raw web
faces under massive real-world noise. Besides discriminative feature embedding,
we also explore the inverse problem, mapping feature vectors to face images.
Without training any additional generator or discriminator, the pre-trained
ArcFace model can generate identity-preserved face images for both subjects
inside and outside the training data only by using the network gradient and
Batch Normalization (BN) priors. Extensive experiments demonstrate that ArcFace
can enhance the discriminative feature embedding as well as strengthen the
generative face synthesis.
