1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
Lights, Camera, Matching: The Role of Image Illumination in Fair Face Recognition
Gabriella Pangelinan, Grace Bezold, Haiyu Wu +2
Facial brightness is a key image quality factor impacting face recognition accuracy differentials across demographic groups. In this work, we aim to decrease the accuracy gap betwe…
Impact of Sunglasses on One-to-Many Facial Identification Accuracy
Sicong Tian, Haiyu Wu, Michael C. King +1
One-to-many facial identification is documented to achieve high accuracy in the case where both the probe and the gallery are "mugshot quality" images. However, an increasing numbe…