Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification

Alexandre Fournier-Montgieux, Michaël Soumm, Adrian Popescu, Bertrand Luvison, Hervé Le Borgne; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 2788-2798

Face recognition and verification are two computer vision tasks whose performances have advanced with the introduction of deep representations. However ethical legal and technical challenges due to the sensitive nature of face data and biases in real-world training datasets hinder their development. Generative AI addresses privacy by creating fictitious identities but fairness problems remain. Using the existing DCFace SOTA framework we introduce a new controlled generation pipeline that improves fairness. Through classical fairness metrics and a proposed in-depth statistical analysis based on logit models and ANOVA we show that our generation pipeline improves fairness more than other bias mitigation approaches while slightly improving raw performance.