Real-world face recognition applications often deal with suboptimal image quality or resolution due to different capturing conditions such as various subject-to-camera distances, poor camera settings, or motion blur. This characteristic has an unignorable effect on performance. Recent cross-resolution face recognition approaches used simple, arbitrary, and unrealistic down- and up-scaling techniques to measure robustness against real-world edge-cases in image quality. Thus, we propose a new standardized benchmark dataset derived from the famous Labeled Faces in the Wild (LFW). In contrast to previous derivatives, which focus on pose, age, similarity, and adversarial attacks, our Cross-Quality Labeled Faces in the Wild (XQLFW) dataset maximizes the quality difference. It contains only more realistic synthetically degraded images when necessary. Our proposed dataset is then used to further investigate the influence of image quality on several state-of-the-art approaches. With XQLFW, we show that these models perform differently in cross-quality cases, and hence, the generalizing capability is not accurately predicted by their performance on LFW. Additionally, we report baseline accuracy with recent deep learning models explicitly trained for cross-resolution applications and evaluate the susceptibility to image quality. To encourage further research in cross-resolution face recognition and incite the assessment of image quality robustness, we publish the database and code for evaluation.

中文翻译：

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Cross-Quality LFW：用于在无约束环境中分析跨分辨率图像人脸识别的数据库

由于不同的拍摄条件（例如不同的拍摄对象到相机的距离、相机设置不佳或运动模糊），现实世界的人脸识别应用程序通常会处理次优图像质量或分辨率。该特性对性能具有不可忽视的影响。最近的跨分辨率人脸识别方法使用简单、任意和不切实际的缩小和放大技术来衡量图像质量对现实世界边缘情况的鲁棒性。因此，我们提出了一个新的标准化基准数据集，该数据集源自著名的 Labeled Faces in the Wild (LFW)。与之前侧重于姿势、年龄、相似性和对抗性攻击的衍生品相比，我们的 Cross-Quality Labeled Faces in the Wild (XQLFW) 数据集最大限度地提高了质量差异。必要时，它只包含更逼真的合成降级图像。然后，我们提出的数据集用于进一步研究图像质量对几种最先进方法的影响。使用 XQLFW，我们表明这些模型在跨质量情况下的表现不同，因此，它们在 LFW 上的表现无法准确预测泛化能力。此外，我们报告了最近针对跨分辨率应用程序明确训练的深度学习模型的基线准确性，并评估了对图像质量的敏感性。为了鼓励对跨分辨率人脸识别的进一步研究并激发对图像质量鲁棒性的评估，我们发布了用于评估的数据库和代码。因此，它们在 LFW 上的性能无法准确预测泛化能力。此外，我们报告了最近针对跨分辨率应用程序明确训练的深度学习模型的基线准确性，并评估了对图像质量的敏感性。为了鼓励对跨分辨率人脸识别的进一步研究并激发对图像质量鲁棒性的评估，我们发布了用于评估的数据库和代码。因此，它们在 LFW 上的性能无法准确预测泛化能力。此外，我们报告了最近针对跨分辨率应用程序明确训练的深度学习模型的基线准确性，并评估了对图像质量的敏感性。为了鼓励对跨分辨率人脸识别的进一步研究并激发对图像质量鲁棒性的评估，我们发布了用于评估的数据库和代码。