26 citations · 35 across the 5 of their papers we have counts for
6 papers
A Covariate-Adjusted Homogeneity Test with Application to Facial Recognition Accuracy Assessment
Ngoc-Ty Nguyen, P. Jonathon Phillips, Larry Tang
Ordinal scores occur commonly in medical imaging studies and in black-box forensic studies \citep{Phillips:2018}. To assess the accuracy of raters in the studies, one needs to esti…
Human-Machine Comparison for Cross-Race Face Verification: Race Bias at the Upper Limits of Performance?
Geraldine Jeckeln, Selin Yavuzcan, Kate A. Marquis +4
Face recognition algorithms perform more accurately than humans in some cases, though humans and machines both show race-based accuracy differences. As algorithms continue to impro…
Distill and De-bias: Mitigating Bias in Face Verification using Knowledge Distillation
Prithviraj Dhar, Joshua Gleason, Aniket Roy +3
Face recognition networks generally demonstrate bias with respect to sensitive attributes like gender, skintone etc. For gender and skintone, we observe that the regions of the fac…
Four Principles of Explainable AI as Applied to Biometrics and Facial Forensic Algorithms
P. Jonathon Phillips, Mark Przybocki
Traditionally, researchers in automatic face recognition and biometric technologies have focused on developing accurate algorithms. With this technology being integrated into opera…
Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?
Jacqueline G. Cavazos, P. Jonathon Phillips, Carlos D. Castillo +1
Previous generations of face recognition algorithms differ in accuracy for images of different races (race bias). Here, we present the possible underlying factors (data-driven and…
How are attributes expressed in face DCNNs?
Prithviraj Dhar, Ankan Bansal, Carlos D. Castillo +3
As deep networks become increasingly accurate at recognizing faces, it is vital to understand how these networks process faces. While these networks are solely trained to recognize…