14 papers · 1 filter
Learning Steadily: Accumulating Relative Point Margin Scores for Face Image Quality Assessment
Guray Ozgur, Tahar Chettaoui, Eduarda Caldeira +4
Face Image Quality Assessment determines the suitability of captured face images for automated face recognition (FR), a critical capability for reliable biometric systems. Existing…
IJCB-AFMFR 2026: Competition on Adapting Foundation Models for Face Recognition Using Synthetic Training Data
Tahar Chettaoui, Guray Ozgur, Eduarda Caldeira +13
This paper presents a summary of the Competition on Adapting Foundation Models for Face Recognition Using Synthetic Training Data (AFMFR), held at the 2026 International Joint Conf…
Vision Transformers for Face Recognition Need More Registers
Tahar Chettaoui, Guray Ozgur, Eduarda Caldeira +2
Recent advances in Vision Transformers (ViTs) for face recognition (FR) have moved beyond the standard CLS-token paradigm. In this paradigm, a special classification token (CLS) is…
ViT-FREE: Efficient Face Recognition via Early Exiting and Synthetic Adaptation
Tahar Chettaoui, Guray Ozgur, Eduarda Caldeira +2
Vision Transformers (ViTs) have gained significant attention in computer vision and shown strong potential for face recognition (FR). However, their high computational cost makes d…
DCMorph: Face Morphing via Dual-Stream Cross-Attention Diffusion
Tahar Chettaoui, Eduarda Caldeira, Guray Ozgur +3
Advancing face morphing attack techniques is crucial to anticipate evolving threats and develop robust defensive mechanisms for identity verification systems. This work introduces…
EX-FIQA: Leveraging Intermediate Early eXit Representations from Vision Transformers for Face Image Quality Assessment
Guray Ozgur, Tahar Chettaoui, Eduarda Caldeira +4
Face Image Quality Assessment is crucial for reliable face recognition systems, yet existing Vision Transformer-based approaches rely exclusively on final-layer representations, ig…
ATTN-FIQA: Interpretable Attention-based Face Image Quality Assessment with Vision Transformers
Guray Ozgur, Tahar Chettaoui, Eduarda Caldeira +5
Face Image Quality Assessment (FIQA) aims to assess the recognition utility of face samples and is essential for reliable face recognition (FR) systems. Existing approaches require…
IDperturb: Enhancing Variation in Synthetic Face Generation via Angular Perturbation
Fadi Boutros, Eduarda Caldeira, Tahar Chettaoui +1
Synthetic data has emerged as a practical alternative to authentic face datasets for training face recognition (FR) systems, especially as privacy and legal concerns increasingly r…
ViTNT-FIQA: Training-Free Face Image Quality Assessment with Vision Transformers
Guray Ozgur, Eduarda Caldeira, Tahar Chettaoui +4
Face Image Quality Assessment (FIQA) is essential for reliable face recognition systems. Current approaches primarily exploit only final-layer representations, while training-free…
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…
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…
FRoundation: Are Foundation Models Ready for Face Recognition?
Tahar Chettaoui, Naser Damer, Fadi Boutros
Foundation models are predominantly trained in an unsupervised or self-supervised manner on highly diverse and large-scale datasets, making them broadly applicable to various downs…