1:n matching 1face identification 1image quality robustness 1rank consensus 1threshold-free decision 1
From the 1 of 8 linked papers with an AI index.
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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 licenses and/or booking photos. The algorithm's rank…
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
The paper proposes using rank‑1 identity consensus across multiple face matchers (1‑consistency) to decide whether a probe is enrolled in a gallery, showing it matches or exceeds t…
cs.CV2026
Goldilocks Test Sets for Face Verification
Haiyu Wu, Sicong Tian, Aman Bhatta +6
Reported face verification accuracy has reached a plateau on current well-known test sets. As a result, some difficult test sets have been assembled by reducing the image quality o…