Facial Attribute Classification (FAC) holds substantial promise in widespread applications. However, FAC models trained by traditional methodologies can be unfair by exhibiting accuracy inconsistencies across varied data subpopulations. This unfairness is largely attributed to bias in data, where some spurious attributes (e.g., Male) statistically correlate with the target attribute (e.g., Smiling). Most of existing fairness-aware methods rely on the labels of spurious attributes, which may be unavailable in practice. This work proposes a novel, generation-based two-stage framework to train a fair FAC model on biased data without additional annotation. Initially, we identify the potential spurious attributes based on generative models. Notably, it enhances interpretability by explicitly showing the spurious attributes in image space. Following this, for each image, we first edit the spurious attributes with a random degree sampled from a uniform distribution, while keeping target attribute unchanged. Then we train a fair FAC model by fostering model invariance to these augmentation. Extensive experiments on three common datasets demonstrate the effectiveness of our method in promoting fairness in FAC without compromising accuracy. Codes are in https://github.com/heqianpei/DiGA.

翻译：面部属性分类（Facial Attribute Classification, FAC）在广泛应用中具有重要潜力。然而，基于传统方法训练的面部属性分类模型可能因在不同数据子群体间表现出的准确性差异而产生不公平性。这种不公平性主要源于数据偏差，即某些虚假属性（如“男性”）与目标属性（如“微笑”）在统计上存在相关性。现有大多数公平性感知方法依赖虚假属性的标签，但在实际应用中这些标签可能无法获取。本文提出一种新颖的、基于生成的两阶段框架，无需额外标注即可在偏差数据上训练公平的面部属性分类模型。首先，我们基于生成模型识别潜在的虚假属性。值得注意的是，通过明确展示图像空间中的虚假属性，该方法增强了可解释性。随后，对于每张图像，我们在保持目标属性不变的前提下，以均匀分布采样的随机程度编辑虚假属性，然后通过促进模型对这些增强的鲁棒性来训练公平的面部属性分类模型。在三个公开数据集上的大量实验表明，我们的方法在不牺牲准确性的情况下，有效促进了面部属性分类的公平性。代码地址：https://github.com/heqianpei/DiGA。