构建大规模人脸公平性与鲁棒性评估基准，助力更可靠的AI系统
Face4FairShifts: A Large Image Benchmark for Fairness and Robust Learning across Visual Domains
- 构建跨域人脸数据集，含10万张图像与39个属性标注
- 在4个视觉差异显著的域上发现模型性能显著下降
- 适合关注公平性、域泛化与人脸识别的研究者
确保机器学习模型的公平性与鲁棒性仍是挑战，尤其在域偏移下。我们提出Face4FairShifts，一个大规模人脸图像基准，用于系统评估公平性感知学习与域泛化能力。该数据集包含10万张图像，覆盖4个视觉差异显著的域，包含14个属性的39个标注，涵盖人口统计与面部特征。通过大量实验，我们分析了模型在分布偏移下的表现，并发现显著性能差距。研究结果凸显现有相关数据集的局限性，强调需要更有效的公平性感知域适应技术。Face4FairShifts为推动公平可靠AI系统提供了全面测试平台。数据集可在线获取：https://meviuslab.github.io/Face4FairShifts/。
原文摘要 · Abstract (English)
Ensuring fairness and robustness in machine learning models remains a challenge, particularly under domain shifts. We present Face4FairShifts, a large-scale facial image benchmark designed to systematically evaluate fairness-aware learning and domain generalization. The dataset includes 100,000 images across four visually distinct domains with 39 annotations within 14 attributes covering demographic and facial features. Through extensive experiments, we analyze model performance under distribution shifts and identify significant gaps. Our findings emphasize the limitations of existing related datasets and the need for more effective fairness-aware domain adaptation techniques. Face4FairShifts provides a comprehensive testbed for advancing equitable and reliable AI systems. The dataset is available online at https://meviuslab.github.io/Face4FairShifts/.
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