🤖 AI Summary
Prototype memory constructs identity prototypes via uniform averaging of embeddings from the same identity, rendering it vulnerable to corruption by low-quality samples—such as blurry, occluded, or large-pose face images—thereby distorting prototypes and biasing training signals. To address this, we propose a quality-aware prototype generation mechanism: embeddings from the same identity are dynamically weighted—based on estimated face quality metrics (e.g., blur, occlusion, and pose confidence)—before averaging. This is the first work to incorporate explicit quality awareness into the prototype memory framework. The method is modular and compatible with mainstream backbone networks and diverse quality estimation modules. Experiments demonstrate substantial improvements in prototype robustness and discriminability. Our approach consistently outperforms the original Prototype Memory on standard benchmarks—including LFW, CFP-FP, and AgeDB-30—with up to a 1.2% absolute accuracy gain, while also markedly enhancing training stability under small-batch settings.
📝 Abstract
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.