  Learning discriminative face features plays a major role in building
high-performing face recognition models. The recent state-of-the-art face
recognition solutions proposed to incorporate a fixed penalty margin on
commonly used classification loss function, softmax loss, in the normalized
hypersphere to increase the discriminative power of face recognition models, by
minimizing the intra-class variation and maximizing the inter-class variation.
Marginal penalty softmax losses, such as ArcFace and CosFace, assume that the
geodesic distance between and within the different identities can be equally
learned using a fixed penalty margin. However, such a learning objective is not
realistic for real data with inconsistent inter-and intra-class variation,
which might limit the discriminative and generalizability of the face
recognition model. In this paper, we relax the fixed penalty margin constrain
by proposing elastic penalty margin loss (ElasticFace) that allows flexibility
in the push for class separability. The main idea is to utilize random margin
values drawn from a normal distribution in each training iteration. This aims
at giving the decision boundary chances to extract and retract to allow space
for flexible class separability learning. We demonstrate the superiority of our
ElasticFace loss over ArcFace and CosFace losses, using the same geometric
transformation, on a large set of mainstream benchmarks. From a wider
perspective, our ElasticFace has advanced the state-of-the-art face recognition
performance on seven out of nine mainstream benchmarks.
