Face Super-Resolution Quality Assessment Based on Identity and Recognizability

Weiling Chen;Weitao Lin;Xiaoyi Xu;Liqun Lin;Tiesong Zhao
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引用次数: 0

Abstract

Face Super-Resolution (FSR) plays a crucial role in enhancing low-resolution face images, which is essential for various face-related tasks. However, FSR may alter individuals’ identities or introduce artifacts that affect recognizability. This problem has not been well assessed by existing Image Quality Assessment (IQA) methods. In this paper, we present both subjective and objective evaluations for FSR-IQA, resulting in a benchmark dataset and a reduced reference quality metrics, respectively. First, we incorporate a novel criterion of identity preservation and recognizability to develop our Face Super-resolution Quality Dataset (FSQD). Second, we analyze the correlation between identity preservation and recognizability, and investigate effective feature extractions for both of them. Third, we propose a training-free IQA framework called Face Identity and Recognizability Evaluation of Super-resolution (FIRES). Experimental results using FSQD demonstrate that FIRES achieves competitive performance.
基于身份和可识别性的人脸超分辨率质量评估
人脸超分辨率(FSR)在增强低分辨率人脸图像方面发挥着至关重要的作用,这对各种与人脸有关的任务至关重要。然而,FSR 可能会改变个人身份或引入影响可识别性的伪影。现有的图像质量评估(IQA)方法还不能很好地评估这一问题。在本文中,我们对 FSR-IQA 进行了主观和客观评估,分别得出了基准数据集和简化的参考质量指标。首先,我们采用了一种新颖的身份保持和可识别标准来开发人脸超分辨率质量数据集(FSQD)。其次,我们分析了身份保持和可识别性之间的相关性,并研究了针对这两者的有效特征提取方法。第三,我们提出了一种无需训练的 IQA 框架,称为 "超分辨率的人脸身份和可识别性评估(FIRES)"。使用 FSQD 的实验结果表明,FIRES 实现了具有竞争力的性能。
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CiteScore
10.90
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