Face recognition (FR) algorithms have been proven to exhibit discriminatory behaviors against certain demographic and non-demographic groups, raising ethical and legal concerns regarding their deployment in real-world scenarios. Despite the growing number of fairness studies in FR, the fairness of face presentation attack detection (PAD) has been overlooked, mainly due to the lack of appropriately annotated data. To avoid and mitigate the potential negative impact of such behavior, it is essential to assess the fairness in face PAD and develop fair PAD models. To enable fairness analysis in face PAD, we present a Combined Attribute Annotated PAD Dataset (CAAD-PAD), offering seven human-annotated attribute labels. Then, we comprehensively analyze the fairness of PAD and its relation to the nature of the training data and the Operational Decision Threshold Assignment (ODTA) through a set of face PAD solutions. Additionally, we propose a novel metric, the Accuracy Balanced Fairness (ABF), that jointly represents both the PAD fairness and the absolute PAD performance. The experimental results pointed out that female and faces with occluding features (e.g. eyeglasses, beard, etc.) are relatively less protected than male and non-occlusion groups by all PAD solutions. To alleviate this observed unfairness, we propose a plug-and-play data augmentation method, FairSWAP, to disrupt the identity/semantic information and encourage models to mine the attack clues. The extensive experimental results indicate that FairSWAP leads to better-performing and fairer face PADs in 10 out of 12 investigated cases.

翻译：人脸识别（FR）算法已被证明对某些人口统计和非人口统计群体表现出歧视性行为，这引发了将其部署到现实场景时的伦理和法律问题。尽管FR领域的公平性研究日益增多，但人脸呈现攻击检测（PAD）的公平性问题仍被忽视，主要原因在于缺乏适当标注的数据。为避免并减轻此类行为的潜在负面影响，评估人脸PAD中的公平性并开发公平的PAD模型至关重要。为支持人脸PAD中的公平性分析，我们提出了一个组合属性标注的PAD数据集（CAAD-PAD），提供了七种人工标注的属性标签。随后，我们通过一系列人脸PAD解决方案，全面分析了PAD的公平性及其与训练数据本质和操作决策阈值分配（ODTA）的关系。此外，我们提出了一种新型指标——精度平衡公平性（ABF），该指标同时表征了PAD公平性和PAD绝对性能。实验结果表明，所有PAD解决方案对女性和具有遮挡特征（如眼镜、胡须等）的人脸提供的保护相对弱于男性和无遮挡群体。为缓解这一观察到的不公平性，我们提出了一种即插即用的数据增强方法FairSWAP，以破坏身份/语义信息并鼓励模型挖掘攻击线索。大量实验结果表明，在12个研究案例中，FairSWAP在10个案例中实现了性能更优且更公平的人脸PAD。