  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
$\href{https://github.com/WakingHours-GitHub/EPL}{https://github.com/WakingHours-GitHub/EPL}$.
