XQLFW Dataset

An evaluation protocol for face verification focusing on a large intra-pair image quality difference.

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 and evaluation protocol 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) 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.

针对大型图像质量差异的面对面验证评估协议。现实世界的面部识别应用往往因不同的捕捉条件，如多样的主体与相机距离、不良的相机设置或运动模糊，而面临图像质量或分辨率不理想的问题。此特性对性能产生不可忽视的影响。近期跨分辨率面部识别方法采用简单、任意且不切实际的降级和放大技术，以衡量对现实世界边缘情况图像质量的鲁棒性。因此，我们提出了一种基于著名标记人脸在野外（LFW）数据集的新标准化基准数据集和评估协议。与以往侧重于姿态、年龄、相似性和对抗攻击的衍生版本不同，我们的跨质量标记人脸在野外（XQLFW）最大化了质量差异。当必要时，仅包含更现实的合成退化图像。我们提出的数据集随后被用于进一步研究图像质量对多种最先进方法的影响。借助XQLFW，我们展示了这些模型在跨质量情况下的表现不同，因此，其泛化能力不能准确通过在LFW上的表现预测。此外，我们报告了针对跨分辨率应用专门训练的最近深度学习模型的基线准确率，并评估了对图像质量的敏感性。