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.

翻译：人脸识别与验证是两项计算机视觉任务，其性能随着深度表征的引入而显著提升。然而，由于人脸数据的敏感性以及现实世界训练数据集中存在的偏见，伦理、法律和技术上的挑战阻碍了其发展。生成式人工智能通过创建虚构身份来解决隐私问题，但公平性问题依然存在。基于现有的DCFace SOTA框架，我们引入了一种新的可控生成流程，以提升公平性。通过经典公平性指标以及我们提出的基于对数模型和ANOVA的深入统计分析，我们证明，与其他偏见缓解方法相比，我们的生成流程在略微提升原始性能的同时，能更有效地改善公平性。