56 citations · 58 across the 13 of their papers we have counts for
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cs.CV2026
Rank-1 Identity Consensus Predicts Gallery Enrollment in 1:N Face Matching More Accurately than Score Thresholding
Gabriella Pangelinan, Aman Bhatta, Michael C. King +1
In operational 1:N face identification, a crucial question arises for each probe: is this person enrolled in the gallery or not? The stakes are high and asymmetric. Rejecting a mat…
cs.CV2026
What Happens to Accuracy When Photo Lineups Contain Non-Mated Rank-One Images From Large Galleries?
Genesis Argueta, Kevin W. Bowyer, Michael King +1
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-…