  Prototype Memory is a powerful model for face representation learning. It
enables the training of face recognition models using datasets of any size,
with on-the-fly generation of prototypes (classifier weights) and efficient
ways of their utilization. Prototype Memory demonstrated strong results in many
face recognition benchmarks. However, the algorithm of prototype generation,
used in it, is prone to the problems of imperfectly calculated prototypes in
case of low-quality or poorly recognizable faces in the images, selected for
the prototype creation. All images of the same person, presented in the
mini-batch, used with equal weights, and the resulting averaged prototype could
be contaminated with imperfect embeddings of such face images. It can lead to
misdirected training signals and impair the performance of the trained face
recognition models. In this paper, we propose a simple and effective way to
improve Prototype Memory with quality-aware prototype generation. Quality-Aware
Prototype Memory uses different weights for images of different quality in the
process of prototype generation. With this improvement, prototypes get more
valuable information from high-quality images and less hurt by low-quality
ones. We propose and compare several methods of quality estimation and usage,
perform extensive experiments on the different face recognition benchmarks and
demonstrate the advantages of the proposed model compared to the basic version
of Prototype Memory.
