  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.
