Abstract
The widespread integration of face recognition technologies into various applications (e.g., access control and personalized advertising) necessitates a critical emphasis on fairness. While previous efforts have focused on demographic fairness, the fairness of individual biological face components remains unexplored. In this paper, we focus on face component fairness, a fairness notion defined by biological face features. To our best knowledge, our work is the first work to mitigate bias of face attribute prediction at the biological feature level. In this work, we identify two key challenges in optimizing face component fairness: attribute label scarcity and attribute inter-dependencies, both of which limit the effectiveness of bias mitigation from previous approaches. To address these issues, we propose \textbf{B}ayesian \textbf{N}etwork-informed \textbf{M}eta \textbf{R}eweighting (BNMR), which incorporates a Bayesian Network calibrator to guide an adaptive meta-learning-based sample reweighting process. During the training process of our approach, the Bayesian Network calibrator dynamically tracks model bias and encodes prior probabilities for face component attributes to overcome the above challenges. To demonstrate the efficacy of our approach, we conduct extensive experiments on a large-scale real-world human face dataset. Our results show that BNMR is able to consistently outperform recent face bias mitigation baselines. Moreover, our results suggest a positive impact of face component fairness on the commonly considered demographic fairness (e.g., \textit{gender}). Our findings pave the way for new research avenues on face component fairness, suggesting that face component fairness could serve as a potential surrogate objective for demographic fairness. The code for our work is publicly available~\footnote{this https URL}.
Abstract (translated)
面部识别技术在各种应用（例如访问控制和个人化广告）中的广泛应用，要求对公平性给予高度重视。尽管以前的努力主要集中在人口统计学的公平性上，但关于个体生物面部组件的公平性仍未被探索。在这篇文章中，我们专注于面部组件公平性，这是一种由生物学面部特征定义的公平性概念。据我们所知，我们的工作是首次在生物学特征水平上减轻面部属性预测偏差的研究。 在此工作中，我们确定了优化面部组件公平性的两个关键挑战：属性标签稀缺性和属性间的相互依赖关系，这些都限制了以前方法消除偏见的有效性。为了解决这些问题，我们提出了基于贝叶斯网络的元重加权（BNMR）方法，该方法引入了一个贝叶斯网络校准器来引导一个自适应的元学习样本重新加权过程。在我们的方法训练过程中，贝叶斯网络校准器会动态跟踪模型偏差，并为面部组件属性编码先验概率以克服上述挑战。 为了展示我们方法的有效性，我们在大规模的真实世界人类面部数据集上进行了广泛的实验。结果显示，BNMR能够持续优于最近的面部偏见缓解基线。此外，我们的结果表明，面部组件公平性对通常考虑的人口统计学公平性（例如性别）具有积极影响。我们的发现为面部组件公平性的新研究方向铺平了道路，暗示面部组件公平性可以作为人口统计学公平性的潜在替代目标。我们工作的代码公开可用\footnote{this https URL}。
URL
https://arxiv.org/abs/2505.01699
