Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
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DOI：
10.1073/pnas.1721355115
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发表时间：
2018-06-12
影响因子：
11.1
通讯作者：
O'Toole, Alice J.
中科院分区：
文献类型：
--
作者：
Phillips, P. Jonathon;Yates, Amy N.;O'Toole, Alice J.
在法医应用中达到人脸识别精度的上限可以将具有深远社会和个人后果的错误降至最低。尽管法医在这些应用程序中识别人脸，但对其准确性进行系统测试的情况很少。我们如何才能实现最准确的人脸识别：使用单独工作或协作的人员和/或机器？在对人类和计算机进行人脸识别的综合比较中，我们发现，在一项具有挑战性的人脸识别测试中，法医面部鉴定员、面部鉴定员和超级识别器比指纹鉴定员和学生更准确。个人在测试中的表现差异很大。在同一项测试中，2015至2017年间开发的四个深度卷积神经网络(DCNN)识别出了人类准确度范围内的人脸。随着时间的推移，算法的准确性稳步提高，最近的DCNN得分高于法医面部检查员的中位数。使用众包方法，我们融合了多个法医面部检查员的判断，通过平均他们基于评级的身份判断。融合判断的准确性比单独工作的个人要好得多。融合还起到了稳定表现的作用，提高了表现较差的个体的得分，降低了变异性。与最佳算法融合的单个法医面部检查者比两个检查者的组合更准确。因此，人与人之间以及人与机器之间的协作在重要应用中为人脸识别的准确性提供了实实在在的好处。这些结果为实现尽可能准确的人脸识别提供了一个基于证据的路线图。
Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.