In this paper, we propose a novel explanatory framework aimed to provide a better understanding of how face recognition models perform as the underlying data characteristics (protected attributes: gender, ethnicity, age; non-protected attributes: facial hair, makeup, accessories, face orientation and occlusion, image distortion, emotions) on which they are tested change. With our framework, we evaluate ten state-of-the-art face recognition models, comparing their fairness in terms of security and usability on two data sets, involving six groups based on gender and ethnicity. We then analyze the impact of image characteristics on models performance. Our results show that trends appearing in a single-attribute analysis disappear or reverse when multi-attribute groups are considered, and that performance disparities are also related to non-protected attributes. Source code: https://cutt.ly/2XwRLiA.

翻译：在本文中,我们提出了一个新颖的解释性框架,旨在更好地了解面部识别模型如何作为基本数据特征(受保护的属性:性别、族裔、年龄;非受保护的属性:面部毛发、化妆、配件、面部定向和隔离、图像扭曲、情感)发挥作用,检验这些特征的变化。我们用这个框架评估了10个最先进的面部识别模型,比较了这些模型在安全和可用性方面的公平性,其中涉及6个性别和族裔的数据集。然后我们分析了图像特征对模型性能的影响。我们的结果显示,在单一属性分析中出现的趋势在考虑多属性群体时消失或逆转,业绩差异也与非受保护属性有关。资料来源代码:https://cut.ly/2XwLiA。