Contemporary face recognition (FR) models achieve near-ideal recognition performance in constrained settings, yet do not fully translate the performance to unconstrained (realworld) scenarios. To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality information from the input face images that can aid with the recognition process. While existing FIQA techniques are able to efficiently capture the differences between high and low quality images, they typically cannot fully distinguish between images of similar quality, leading to lower performance in many scenarios. To address this issue, we present in this paper a supervised quality-label optimization approach, aimed at improving the performance of existing FIQA techniques. The developed optimization procedure infuses additional information (computed with a selected FR model) into the initial quality scores generated with a given FIQA technique to produce better estimates of the "actual" image quality. We evaluate the proposed approach in comprehensive experiments with six state-of-the-art FIQA approaches (CR-FIQA, FaceQAN, SER-FIQ, PCNet, MagFace, SDD-FIQA) on five commonly used benchmarks (LFW, CFPFP, CPLFW, CALFW, XQLFW) using three targeted FR models (ArcFace, ElasticFace, CurricularFace) with highly encouraging results.

翻译：当代人脸识别（FR）模型在受约束环境下能够实现接近理想的识别性能，但在非受控（现实）场景中却无法完全迁移这一性能。为帮助提升FR系统在此类非受控环境下的性能与稳定性，人脸图像质量评估（FIQA）技术尝试从输入人脸图像中推断有助于识别过程的样本质量信息。尽管现有FIQA技术能够有效捕捉高质量与低质量图像之间的差异，但通常无法充分区分质量相近的图像，从而导致在许多场景下性能下降。为解决这一问题，本文提出一种有监督的质量标签优化方法，旨在提升现有FIQA技术的性能。所开发的优化过程将（通过选定FR模型计算的）额外信息注入给定FIQA技术生成的初始质量评分中，从而产生对"实际"图像质量更优的估计。我们通过综合实验对所提方法进行评估，实验采用六种最先进的FIQA方法（CR-FIQA、FaceQAN、SER-FIQ、PCNet、MagFace、SDD-FIQA），在五个常用基准数据集（LFW、CFPFP、CPLFW、CALFW、XQLFW）上使用三种目标FR模型（ArcFace、ElasticFace、CurricularFace），取得了极具鼓舞性的结果。