From the 1 of 8 linked papers with an AI index.
8 papers
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…
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…
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…
Are you In or Out (of gallery)? Wisdom from the Same-Identity Crowd
Aman Bhatta, Maria Dhakal, Michael C. King +1
A central problem in one-to-many facial identification is that the person in the probe image may or may not have enrolled image(s) in the gallery; that is, may be In-gallery or Out…
Deep CNN Face Matchers Inherently Support Revocable Biometric Templates
Aman Bhatta, Michael C. King, Kevin W. Bowyer
One common critique of biometric authentication is that if an individual's biometric is compromised, then the individual has no recourse. The concept of revocable biometrics was de…
Peepers & Pixels: Human Recognition Accuracy on Low Resolution Faces
Xavier Merino, Gabriella Pangelinan, Samuel Langborgh +2
Automated one-to-many (1:N) face recognition is a powerful investigative tool commonly used by law enforcement agencies. In this context, potential matches resulting from automated…