公平SA：人脸识别公平性的敏感性分析
Fair SA: Sensitivity Analysis for Fairness in Face Recognition
摘要：
随着深度学习在高影响力领域的使用变得无处不在，评估模型的弹性变得越来越重要。其中一个高影响领域是人脸识别，现实世界的应用涉及受到各种退化（例如运动模糊或高曝光）影响的图像。此外，跨不同属性（例如性别和种族）捕获的图像也会挑战人脸识别算法的鲁棒性。虽然传统的汇总统计表明人脸识别模型的总体性能不断提高，但这些指标并不能直接衡量模型的鲁棒性或公平性。视觉心理物理敏感性分析（VPSA）[1]通过在数据中引入增量扰动，提供了一种查明故障原因的方法。然而，扰动可能会影响不同的子群。在本文中，我们提出了一个新的公平性评估的基础上，鲁棒性的形式，扩展VPSA的通用框架。有了这个框架，我们可以分析模型在受扰动影响的人口的不同子群体中公平执行的能力，并通过测量目标鲁棒性来确定子群体的确切故障模式。随着人们对模型公平性的关注越来越多，我们使用人脸识别作为我们框架的一个示例应用，并建议通过AUC矩阵对模型的公平性分析进行复杂的可视化。我们分析了常见的人脸识别模型的性能，并根据经验表明，当图像受到扰动时，某些子组处于不利地位，从而揭示了使用模型在没有扰动的子组上的性能不可见的趋势。
英文摘要：
As the use of deep learning in high impact domains becomes ubiquitous, it is increasingly important to assess the resilience of models. One such high impact domain is that of face recognition, with real world applications involving images affected by various degradations, such as motion blur or high exposure. Moreover, images captured across different attributes, such as gender and race, can also challenge the robustness of a face recognition algorithm. While traditional summary statistics suggest that the aggregate performance of face recognition models has continued to improve, these metrics do not directly measure the robustness or fairness of the models. Visual Psychophysics Sensitivity Analysis (VPSA) [1] provides a way to pinpoint the individual causes of failure by way of introducing incremental perturbations in the data. However, perturbations may affect subgroups differently. In this paper, we propose a new fairness evaluation based on robustness in the form of a generic framework that extends VPSA. With this framework, we can analyze the ability of a model to perform fairly for different subgroups of a population affected by perturbations, and pinpoint the exact failure modes for a subgroup by measuring targeted robustness. With the increasing focus on the fairness of models, we use face recognition as an example application of our framework and propose to compactly visualize the fairness analysis of a model via AUC matrices. We analyze the performance of common face recognition models and empirically show that certain subgroups are at a disadvantage when images are perturbed, thereby uncovering trends that were not visible using the model's performance on subgroups without perturbations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
