论文标题
生物识别技术公平：评估生物特征验证系统的优点
Fairness in Biometrics: a figure of merit to assess biometric verification systems
论文作者
论文摘要
自从过去十年以来，基于机器学习的系统（ML）系统在无数的场景中都在影响我们日常生活中的几个实例。通过这种广泛的应用，由于少数群体会产生的社会影响，公平性的各个方面开始引起人们的关注。在这项工作中，涉及生物识别技术公平性的方面。首先，我们介绍了能够评估和比较多个生物识别验证系统（所谓的公平差异率（FDR））之间的公平方面的第一个功绩。引入了具有两个合成生物识别系统的用例，并在极端的公平和不公平行为的情况下证明了这一功绩的潜力。其次，介绍了使用面部生物识别技术的用例，其中使用三个探索性别和种族人口统计数据的公共数据集进行了与这个新功绩相比，评估了几个系统。
Machine learning-based (ML) systems are being largely deployed since the last decade in a myriad of scenarios impacting several instances in our daily lives. With this vast sort of applications, aspects of fairness start to rise in the spotlight due to the social impact that this can get in minorities. In this work aspects of fairness in biometrics are addressed. First, we introduce the first figure of merit that is able to evaluate and compare fairness aspects between multiple biometric verification systems, the so-called Fairness Discrepancy Rate (FDR). A use case with two synthetic biometric systems is introduced and demonstrates the potential of this figure of merit in extreme cases of fair and unfair behavior. Second, a use case using face biometrics is presented where several systems are evaluated compared with this new figure of merit using three public datasets exploring gender and race demographics.
