The emergence of COVID-19 has had a global and profound impact, not only on society as a
whole, but also on the lives of individuals. Various prevention measures were introduced …

This paper introduces a new large consent-driven dataset aimed at assisting in the
evaluation of algorithmic bias and robustness of computer vision and audio speech models …

We explore varying face recognition accuracy across demographic groups as a
phenomenon partly caused by differences in face illumination. We observe that for a …

Diffusion models are becoming increasingly popular in synthetic data generation and image
editing applications. However, these models can amplify existing biases and propagate …

Facial forgery by deepfakes has raised severe societal concerns. Several solutions have
been proposed by the vision community to effectively combat the misinformation on the …

Modern deep CNN face matchers are trained on datasets containing" color" images. We
show that such matchers achieve essentially the same accuracy on color images when …

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 known …

Face attribute research has so far used only simple binary attributes for facial hair; eg,
beard/no beard. We have created a new, more descriptive facial hair annotation scheme …

Bias and fairness of biometric algorithms have been key topics of research in recent years,
mainly due to the societal, legal and ethical implications of potentially unfair decisions made …

Human-centric computer vision (HCCV) data curation practices often neglect privacy and
bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed …