当照片列队辨认中的嫌疑图像来自大型图库中的非匹配一阶图像时，准确率会如何变化？
What Happens to Accuracy When Photo Lineups Contain Non-Mated Rank-One Images From Large Galleries?
- University of Notre Dame（圣母大学）
- Florida Institute of Technology（佛罗里达理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究一对多面部识别中，证人错误识别概率与图库大小的关系，通过比较不同图库规模下照片列队准确率，发现图库越大证人误识可能性及信心越高，引发对面部识别图像用于列队辨认及结果作为逮捕依据的思考。
AI中文摘要：
一对多面部识别常用于将监控视频中的探测图像与驾照和/或登记照片图库进行匹配。图库中的一阶图像或人类审查员从算法排名靠前的图像中选出的图像，可能会被放入向证人展示的照片列队中。证人在照片列队中选择图库图像可能直接导致图库图像中的人被捕。这种面部识别过程至少涉及9起错误逮捕。这项工作专门研究证人做出错误识别的概率是否会随着搜索图库的大小而增加。我们比较了“嫌疑”图像取自500、5000和24000张图像的图库时照片列队的准确率。我们发现，更大的图库会增加证人做出错误识别的可能性以及他们对该（错误）识别的信心。这些结果引发了关于这种面部识别过程产生的图像是否应用于照片列队辨认，以及仅照片列队辨认的结果是否应构成逮捕的合理依据的问题。
英文摘要：
One-to-many facial identification is commonly used to match a probe image from surveillance video against a gallery of driver's license and/or booking photos. The algorithm's rank-one image from the gallery, or a human examiner's selection from the algorithm's top-ranked images, may then be placed in a photo lineup shown to a witness. Witness selection of the gallery image in the photo lineup may then lead directly to the person in the gallery image being arrested. This facial identification process is involved in a number of wrongful arrests. This work specifically examines whether the probability of a witness making an incorrect identification increases with the size of the gallery searched. We compare photo lineup accuracy when the "suspect" image is drawn from galleries of 500, 5,000, and 24,000 images. We find that larger galleries increase both the likelihood of a witness making an incorrect identification and their confidence in that (incorrect) identification. These results raise questions of whether an image resulting from such a facial identification process should be used in photo lineups and of whether results of a photo lineup alone should constitute probable cause for arrest.
