56 citations · 58 across the 13 of their papers we have counts for
13 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
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…
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-…
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…
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…
Analysis of Adversarial Image Manipulations
Ahsi Lo, Gabriella Pangelinan, Michael C. King
As virtual and physical identity grow increasingly intertwined, the importance of privacy and security in the online sphere becomes paramount. In recent years, multiple news storie…
Exploring Causes of Demographic Variations In Face Recognition Accuracy
Gabriella Pangelinan, K. S. Krishnapriya, Vitor Albiero +5
In recent years, media reports have called out bias and racism in face recognition technology. We review experimental results exploring several speculated causes for asymmetric cro…
The Gender Gap in Face Recognition Accuracy Is a Hairy Problem
Aman Bhatta, Vítor Albiero, Kevin W. Bowyer +1
It is broadly accepted that there is a "gender gap" in face recognition accuracy, with females having higher false match and false non-match rates. However, relatively little is kn…
Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem
Haiyu Wu, Vítor Albiero, K. S. Krishnapriya +2
We explore varying face recognition accuracy across demographic groups as a phenomenon partly caused by differences in face illumination. We observe that for a common operational s…
Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology
Vítor Albiero, Kai Zhang, Michael C. King +1
Media reports have accused face recognition of being ''biased'', ''sexist'' and ''racist''. There is consensus in the research literature that face recognition accuracy is lower fo…