17 citations · 37 across the 5 of their papers we have counts for
5 papers
DifFoundMAD: Foundation Models meet Differential Morphing Attack Detection
Lazaro J. Gonzalez-Soler, André Dörsch, Christian Rathgeb +1
In this work, we introduce DifFoundMAD, a parameter-efficient D-MAD framework that exploits the generalisation capabilities of vision foundation models (FM) to capture discrepancie…
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…
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…
Synthetic Data for the Mitigation of Demographic Biases in Face Recognition
Pietro Melzi, Christian Rathgeb, Ruben Tolosana +5
This study investigates the possibility of mitigating the demographic biases that affect face recognition technologies through the use of synthetic data. Demographic biases have th…
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez +44
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must…