  Face recognition has made tremendous progress in recent years due to the
advances in loss functions and the explosive growth in training sets size. A
properly designed loss is seen as key to extract discriminative features for
classification. Several margin-based losses have been proposed as alternatives
of softmax loss in face recognition. However, two issues remain to consider: 1)
They overlook the importance of hard sample mining for discriminative learning.
2) Label noise ubiquitously exists in large-scale datasets, which can seriously
damage the model's performance. In this paper, starting from the perspective of
decision boundary, we propose a novel mining framework that focuses on the
relationship between a sample's ground truth class center and its nearest
negative class center. Specifically, a closed-set noise label self-correction
module is put forward, making this framework work well on datasets containing a
lot of label noise. The proposed method consistently outperforms SOTA methods
in various face recognition benchmarks. Training code has been released at
https://github.com/SWJTU-3DVision/BoundaryFace.
