大画廊增加错误指认概率，且让人更自信
What Happens to Accuracy When Photo Lineups Contain Non-Mated Rank-One Images From Large Galleries?
- 对比500、5000、24000张图像画廊，测试指认准确率
- 画廊越大，错误指认率越高，且错误时信心更强
- 警示：仅凭指认结果不应作为逮捕依据
一对一多人脸识别常用于将监控视频中的目标图像与驾照或逮捕照片库进行匹配。算法返回的最高排名图像，或人工从前列图像中选择的图像，可能被放入照片线图供目击者辨认。目击者选择该图像后，可能直接导致该图像中的人被捕。此流程已涉及至少9起误捕事件。本文特别考察了在更大画廊中搜索时，目击者做出错误辨认的概率是否上升。比较了嫌疑图像来自500、5,000和24,000张图像画廊时的照片线图准确率。结果显示，更大的画廊不仅提高错误辨认的可能性，还增强了目击者对错误辨认的信心。这些发现质疑了是否应使用此类人脸识别结果进行照片线图，以及照片线图结果本身是否足以构成逮捕的合理根据。
原文摘要 · Abstract (English)
One-to-many facial identification is commonly used to match a probe image from surveillance video against a gallery of driver's licenses 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 at least 9 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.
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