Prototype learning is widely used in face recognition, which takes the row
vectors of coefficient matrix in the last linear layer of the feature
extraction model as the prototypes for each class. When the prototypes are
updated using the facial sample feature gradients in the model training, they
are prone to being pulled away from the class center by the hard samples,
resulting in decreased overall model performance. In this paper, we explicitly
define prototypes as the expectations of sample features in each class and
design the empirical prototypes using the existing samples in the dataset. We
then devise a strategy to adaptively update these empirical prototypes during
the model training based on the similarity between the sample features and the
empirical prototypes. Furthermore, we propose an empirical prototype learning
(EPL) method, which utilizes an adaptive margin parameter with respect to
sample features. EPL assigns larger margins to the normal samples and smaller
margins to the hard samples, allowing the learned empirical prototypes to
better reflect the class center dominated by the normal samples and finally
pull the hard samples towards the empirical prototypes through the learning.
The extensive experiments on MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace
demonstrate the effectiveness of EPL. Our code is available at
$\href{https://github.com/WakingHours-GitHub/EPL}{this https URL}$.

在这篇论文中，我们将原型学习定义为每个类别中样本特征的期望，并使用数据集中的现有样本设计经验性原型。然后，我们提出了一种自适应更新经验性原型的策略，该策略基于样本特征与经验性原型之间的相似性。此外，我们提出了一种经验性原型学习（EPL）方法，该方法利用与样本特征相关的自适应边缘参数。EPL 为正常样本分配较大的边缘，为困难样本分配较小的边缘，使得学习得到的经验性原型能够更好地反映由正常样本主导的类别中心，并通过学习将困难样本拉向经验性原型。对 MFR、IJB-C、LFW、CFP-FP、AgeDB 和 MegaFace 进行了大量实验证明了 EPL 的有效性。