58 citations · 112 across the 19 of their papers we have counts for
7 papers · 1 filter
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)…
Massively Annotated Datasets for Assessment of Synthetic and Real Data in Face Recognition
Pedro C. Neto, Rafael M. Mamede, Carolina Albuquerque +2
Face recognition applications have grown in parallel with the size of datasets, complexity of deep learning models and computational power. However, while deep learning models evol…
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +55
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class v…
Model Compression Techniques in Biometrics Applications: A Survey
Eduarda Caldeira, Pedro C. Neto, Marco Huber +2
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is h…