论文标题
IFQA：可解释的面部质量评估
IFQA: Interpretable Face Quality Assessment
论文作者
论文摘要
现有的面部修复模型依赖于一般评估指标，这些指标不考虑面部区域的特征。因此，最近的工作已经使用人类研究评估了他们的方法，这是不可扩展的，涉及大量精力。本文提出了一种基于对抗性框架的新颖以面部为中心的指标，在该框架中，发电机模拟面部恢复和歧视器评估图像质量。具体而言，我们的每个像素歧视者可以实现传统指标无法提供的可解释评估。此外，我们的指标强调面部主要区域，因为即使对眼睛，鼻子和口腔的微小变化也会显着影响人类认知。我们面向的指标始终通过令人印象深刻的利润率超过现有的一般或面部图像质量评估指标。我们在各种建筑设计和具有挑战性的场景中证明了拟议策略的普遍性。有趣的是，我们发现我们的IFQA可以导致性能提高作为目标功能。
Existing face restoration models have relied on general assessment metrics that do not consider the characteristics of facial regions. Recent works have therefore assessed their methods using human studies, which is not scalable and involves significant effort. This paper proposes a novel face-centric metric based on an adversarial framework where a generator simulates face restoration and a discriminator assesses image quality. Specifically, our per-pixel discriminator enables interpretable evaluation that cannot be provided by traditional metrics. Moreover, our metric emphasizes facial primary regions considering that even minor changes to the eyes, nose, and mouth significantly affect human cognition. Our face-oriented metric consistently surpasses existing general or facial image quality assessment metrics by impressive margins. We demonstrate the generalizability of the proposed strategy in various architectural designs and challenging scenarios. Interestingly, we find that our IFQA can lead to performance improvement as an objective function.
