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.

翻译：特征学习是广泛应用于大规模人脸识别的方法。近年来，大间隔Softmax损失方法在深度人脸识别中展现出显著性能提升。这类方法通过引入固定正间隔来增强类内紧凑性和类间差异性。然而，现有方法大多未考虑类别不平衡问题——这一在实际开发深度人脸识别模型时面临的主要挑战。我们假设该问题会显著影响深度人脸模型的泛化能力。基于这一观察，我们提出一种名为KappaFace的新型自适应策略，能够根据类别难易程度与不平衡性动态调节相对重要性。借助冯·米塞斯-费舍尔分布的支持，本文提出的KappaFace损失可针对难学习或低聚集度类别增大间隔幅度，同时放宽对逆反类别的约束。在主流人脸基准数据集上的实验表明，该方法取得了优于当前先进技术的性能。