[Submitted on 23 Jul 2026]
Title:What Happens to Accuracy When Photo Lineups Contain Non-Mated Rank-One Images From Large Galleries?
View a PDF of the paper titled What Happens to Accuracy When Photo Lineups Contain Non-Mated Rank-One Images From Large Galleries?, by Genesis Argueta and 3 other authors
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Abstract: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.
Comments: 22 pages, 18 figures, the last 8 pages are figures of suspects used
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.21792 [cs.CV]
(or arXiv:2607.21792v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.21792
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Genesis Argueta [view email] [v1] Thu, 23 Jul 2026 20:17:39 UTC (1,627 KB)
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