3 citations · 3 across the 6 of their papers we have counts for
8 papers · 1 filter
Balancing Beyond Discrete Categories: Continuous Demographic Labels for Fair Face Recognition
Pedro C. Neto, Naser Damer, Jaime S. Cardoso +1
Bias has been a constant in face recognition models. Over the years, researchers have looked at it from both the model and the data point of view. However, their approach to mitiga…
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +56
Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including…
How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face Recognition
Pedro C. Neto, Ivona Colakovic, Sašo Karakatič +1
Leveraging the capabilities of Knowledge Distillation (KD) strategies, we devise a strategy to fight the recent retraction of face recognition datasets. Given a pretrained Teacher…
MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition
Eduarda Caldeira, Jaime S. Cardoso, Ana F. Sequeira +1
As in school, one teacher to cover all subjects is insufficient to distill equally robust information to a student. Hence, each subject is taught by a highly specialised teacher. F…
Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognition
Rafael M. Mamede, Pedro C. Neto, Ana F. Sequeira
This study investigates the effects of occlusions on the fairness of face recognition systems, particularly focusing on demographic biases. Using the Racial Faces in the Wild (RFW)…
FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
Pedro C. Neto, Fadi Boutros, João Ribeiro Pinto +3
SARS-CoV-2 has presented direct and indirect challenges to the scientific community. One of the most prominent indirect challenges advents from the mandatory use of face masks in a…