LossFIQA: A Shortcut Solution to Image Quality Assessment Using Loss for Faces and Beyond

IF 3.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access Pub Date : 2025-07-16 DOI:10.1109/ACCESS.2025.3589778

Marek Vaško;Adam Herout

引用次数: 0

Abstract

We introduce a novel approach to model-based quality assessment of input images. Our approach is very simple, and we demonstrate experimentally that it is not limited to a single domain (typically face recognition in the literature). Our approach generates per-sample quality pseudo-labels directly from the objective function used during the training of the target model. We evaluate the proposed method on eight large and respected datasets (from the face recognition on LFW, CALFW, CPLFW, XQLFW, CFP-FP, AgeDB, IJB-C, and retinopathy detection domain on EyePACS dataset) and using multiple state-of-the-art models (AdaFace, MagFace, ArcFace, ElasticFace, and CuricularFace). Compared to state-of-the-art methods for face quality assessment that are considerably more complex, our solution yields competitive results while being much simpler and not limited to one application.

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LossFIQA：一个快捷的解决方案，以图像质量评估使用损失的面孔和超越

我们介绍了一种基于模型的输入图像质量评估方法。我们的方法非常简单，并且我们通过实验证明它不局限于单一领域（通常是文献中的人脸识别）。我们的方法直接从目标模型训练过程中使用的目标函数生成每个样本质量的伪标签。我们在8个大型且可靠的数据集（来自LFW、CALFW、CPLFW、XQLFW、CFP-FP、AgeDB、ij - c和EyePACS数据集的视网膜病变检测域）上评估了所提出的方法，并使用了多个最先进的模型（adface、MagFace、ArcFace、ElasticFace和CuricularFace）。与最先进的面部质量评估方法相比，这些方法要复杂得多，我们的解决方案产生了具有竞争力的结果，同时更简单，不局限于一个应用程序。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

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