212 citations · 436 across the 99 of their papers we have counts for
13 papers · 2 filters
Mitigating Bias with Words: Inducing Demographic Ambiguity in Face Recognition Templates by Text Encoding
Tahar Chettaoui, Naser Damer, Fadi Boutros
Face recognition (FR) systems are often prone to demographic biases, partially due to the entanglement of demographic-specific information with identity-relevant features in facial…
Trade-offs in Cross-Domain Generalization of Foundation Model Fine-Tuned for Biometric Applications
Tahar Chettaoui, Naser Damer, Fadi Boutros
Foundation models such as CLIP have demonstrated exceptional zero- and few-shot transfer capabilities across diverse vision tasks. However, when fine-tuned for highly specialized b…
ViT-FIQA: Assessing Face Image Quality using Vision Transformers
Andrea Atzori, Fadi Boutros, Naser Damer
Face Image Quality Assessment (FIQA) aims to predict the utility of a face image for face recognition (FR) systems. State-of-the-art FIQA methods mainly rely on convolutional neura…
NegFaceDiff: The Power of Negative Context in Identity-Conditioned Diffusion for Synthetic Face Generation
Eduarda Caldeira, Naser Damer, Fadi Boutros
The use of synthetic data as an alternative to authentic datasets in face recognition (FR) development has gained significant attention, addressing privacy, ethical, and practical…
MADPromptS: Unlocking Zero-Shot Morphing Attack Detection with Multiple Prompt Aggregation
Eduarda Caldeira, Fadi Boutros, Naser Damer
Face Morphing Attack Detection (MAD) is a critical challenge in face recognition security, where attackers can fool systems by interpolating the identity information of two or more…
Second Competition on Presentation Attack Detection on ID Card
Juan E. Tapia, Mario Nieto, Juan M. Espin +30
This work summarises and reports the results of the second Presentation Attack Detection competition on ID cards. This new version includes new elements compared to the previous on…
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…
DiffProb: Data Pruning for Face Recognition
Eduarda Caldeira, Jan Niklas Kolf, Naser Damer +1
Face recognition models have made substantial progress due to advances in deep learning and the availability of large-scale datasets. However, reliance on massive annotated dataset…
SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning
Marija Ivanovska, Leon Todorov, Naser Damer +3
With the continuous advancement of generative models, face morphing attacks have become a significant challenge for existing face verification systems due to their potential use in…
ID-Booth: Identity-consistent Face Generation with Diffusion Models
Darian Tomašević, Fadi Boutros, Chenhao Lin +3
Recent advances in generative modeling have enabled the generation of high-quality synthetic data that is applicable in a variety of domains, including face recognition. Here, stat…
Frequency Matters: Explaining Biases of Face Recognition in the Frequency Domain
Marco Huber, Fadi Boutros, Naser Damer
Face recognition (FR) models are vulnerable to performance variations across demographic groups. The causes for these performance differences are unclear due to the highly complex…
MADation: Face Morphing Attack Detection with Foundation Models
Eduarda Caldeira, Guray Ozgur, Tahar Chettaoui +5
Despite the considerable performance improvements of face recognition algorithms in recent years, the same scientific advances responsible for this progress can also be used to cre…
FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection
Guray Ozgur, Eduarda Caldeira, Tahar Chettaoui +3
Although face recognition systems have seen a massive performance enhancement in recent years, they are still targeted by threats such as presentation attacks, leading to the need…