From the 1 of 5 linked papers with an AI index.
5 papers
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…
Restricted Receptive Fields for Face Verification
Kagan Ozturk, Aman Bhatta, Haiyu Wu +2
Understanding how deep neural networks make decisions is crucial for analyzing their behavior and diagnosing failure cases. In computer vision, a common approach to improve interpr…
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…