Feature learning is a widely used method employed for large-scale face
recognition. Recently, large-margin softmax loss methods have demonstrated
significant enhancements on deep face recognition. These methods propose fixed
positive margins in order to enforce intra-class compactness and inter-class
diversity. However, the majority of the proposed methods do not consider the
class imbalance issue, which is a major challenge in practice for developing
deep face recognition models. We hypothesize that it significantly affects the
generalization ability of the deep face models. Inspired by this observation,
we introduce a novel adaptive strategy, called KappaFace, to modulate the
relative importance based on class difficultness and imbalance. With the
support of the von Mises-Fisher distribution, our proposed KappaFace loss can
intensify the margin's magnitude for hard learning or low concentration classes
while relaxing it for counter classes. Experiments conducted on popular facial
benchmarks demonstrate that our proposed method achieves superior performance
to the state-of-the-art.

通过 KappaFace 算法和 von Mises-Fisher 分布相结合，提出了一种考虑类不平衡问题的大规模人脸识别方法，该算法有效提高了深度人脸识别的性能。