Component-Based Fairness in Face Attribute Classification with Bayesian Network-informed Meta Learning

2025-05-03

AI Summary

This paper addresses fairness disparities in facial attribute classification arising from scarce attribute annotations and inter-part dependencies, introducing the novel concept of “facial-part-level fairness”—a biologically grounded notion focusing on anatomically defined facial components (e.g., eyes, nose). To this end, we propose BNMR: a method integrating a Bayesian network calibrator with a meta-learning-based reweighting mechanism to dynamically track bias and perform prior-guided adaptive sample weighting. BNMR jointly models facial part decomposition, multi-attribute reasoning, and biologically inspired structural constraints. Evaluated on large-scale real-world face datasets, it significantly outperforms state-of-the-art debiasing baselines. Empirical results further demonstrate that improving fairness at the facial-part level positively transfers to demographic fairness—e.g., mitigating gender bias. The implementation is publicly available.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 extbf{B}ayesian extbf{N}etwork-informed extbf{M}eta extbf{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., extit{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{https://github.com/yliuaa/BNMR-FairCompFace.git}.

Problem

• Addressing fairness in face attribute classification at biological feature level
• Overcoming attribute label scarcity and inter-dependencies for bias mitigation
• Proposing meta-learning with Bayesian Network to improve face component fairness

Innovation

• Bayesian Network calibrator tracks model bias
• Meta-learning adaptively reweights training samples
• Encodes prior probabilities for face components