We know from previous research that unfamiliar face matching (determining whether two simultaneously presented images show the same person or not) is very error-prone. A small number of studies in laboratory settings have shown that the use of multiple images or a face average, rather than a single image, can improve face matching performance. Here, we tested 1,999 participants using four-image arrays and face averages in two separate live matching tasks. Matching a single image to a live person resulted in numerous errors (79.9% accuracy across both experiments), and neither multiple images (82.4% accuracy) nor face averages (76.9% accuracy) improved performance. These results are important when considering possible alterations which could be made to photo-ID. Although multiple images and face averages have produced measurable improvements in performance in recent laboratory studies, they do not produce benefits in a real-world live face matching context.

中文翻译：

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实时匹配任务中的人脸平均和多张图像。

从以前的研究中我们知道，陌生的面部匹配（确定两个同时显示的图像是否显示同一个人）非常容易出错。实验室设置中的少量研究表明，使用多张图像或一张脸部平均值而不是单个图像可以改善脸部匹配性能。在这里，我们测试了1,999名参与者，他们使用了四幅图像阵列并在两个单独的实时匹配任务中使用了脸部平均值。将单个图像匹配到一个活着的人会导致许多错误（两个实验中的准确度均为79.9％），并且多幅图像（准确度为82.4％）和面部平均值（准确度为76.9％）都无法提高性能。当考虑可能对photo-ID进行的更改时，这些结果非常重要。