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Feature learning is a widely used method employed for large-scale facerecognition. Recently, large-margin softmax loss methods have demonstratedsignificant enhancements on deep face recognition. These methods propose fixedpositive margins in order to enforce intra-class compactness and inter-classdiversity. However, the majority of the proposed methods do not consider theclass imbalance issue, which is a major challenge in practice for developingdeep face recognition models. We hypothesize that it significantly affects thegeneralization ability of the deep face models. Inspired by this observation,we introduce a novel adaptive strategy, called KappaFace, to modulate therelative importance based on class difficultness and imbalance. With thesupport of the von Mises-Fisher distribution, our proposed KappaFace loss canintensify the margin's magnitude for hard learning or low concentration classeswhile relaxing it for counter classes. Experiments conducted on popular facialbenchmarks demonstrate that our proposed method achieves superior performanceto the state-of-the-art.

KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition