Abstract
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.
Abstract (translated)
当代人脸识别(FR)模型在约束条件下实现接近 ideal 的识别性能,但并未将性能完全转化为无约束(实际)场景。为了在无约束条件下改善 FR 系统的性能与稳定性,人脸图像质量评估(FIQA)技术试图从输入人脸图像中推断样本质量信息,以协助识别过程。尽管现有的FIQA技术能够高效捕捉高质量图像与低质量图像之间的差异,但它们通常无法完全区分相似质量的图像,导致在许多场景中性能下降。为了解决这一问题,本文提出了一种监督质量标签优化方法,旨在改善现有FIQA技术的性能。该开发的优化程序将额外的信息(通过选择 FR 模型计算)注入到给定的FIQA技术生成的初始质量得分中,以产生更准确的“实际”图像质量估计。我们综合了使用六个最先进的FIQA方法(CR-FIQA、FaceQAN、 SER-FIQ、PCNet、 MagFace、SDD-FIQA)在五个常用的基准(LFW、CFPFP、 CPLFW、CALFW、XQLFW)上使用三个目标 FR 模型(ArcFace、 ElasticFace、 curricularFace)并取得高度令人鼓舞的结果进行评估。
URL
https://arxiv.org/abs/2305.14856