33 citations · 138 across the 66 of their papers we have counts for
71 papers
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…
Tomatoes, Potatoes, and Onions: Questioning the Need for Faces in Face Presentation Attack Detection
Guray Ozgur, Fadi Boutros, Naser Damer
Face presentation attack detection (PAD) is traditionally formulated as a face-specific problem, although many of the visual artifacts introduced by print, replay, and recapture pr…
EXPL-FR: Explaining Face Recognition Models via Vision-Language Alignment
Guray Ozgur, Mustafa Efe Tamyapar, Naser Damer +1
Deep face recognition (FR) models reach near-saturated accuracy but remain opaque: a practitioner cannot ask which semantic attributes a similarity score relied upon. EXPL-FR answe…
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…
PreFIQs: Face Image Quality Is What Survives Pruning
Jan Niklas Kolf, Guray Ozgur, Andrea Atzori +4
Face Image Quality Assessment (FIQA) evaluates the utility of a face image for automated face recognition (FR) systems. In this work, we propose PreFIQs, an unsupervised and traini…
FunFace: Feature Utility and Norm Estimation for Face Recognition
Žiga Babnik, Fadi Boutros, Naser Damer +3
Face Recognition (FR) is used in a variety of application domains, from entertainment and banking to security and surveillance. Such applications rely on the FR model to be robust…
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…
On the Impact of Face Segmentation-Based Background Removal on Recognition and Morphing Attack Detection
Eduarda Caldeira, Guray Ozgur, Fadi Boutros +1
This study investigates the impact of face image background correction through segmentation on face recognition and morphing attack detection performance in realistic, unconstraine…
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…
AdaptDiff: Adaptive Guidance in Diffusion Models for Diverse and Identity-Consistent Face Synthesis (Student Abstract)
Eduarda Caldeira, Tahar Chettaoui, Naser Damer +1
Diffusion models conditioned on identity embeddings enable the generation of synthetic face images that consistently preserve identity across multiple samples. Recent work has show…
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…
BLENDER: Blended Text Embeddings and Diffusion Residuals for Intra-Class Image Synthesis in Deep Metric Learning
Jan Niklas Kolf, Ozan Tezcan, Justin Theiss +7
The rise of Deep Generative Models (DGM) has enabled the generation of high-quality synthetic data. When used to augment authentic data in Deep Metric Learning (DML), these synthet…
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…
Beyond Real Faces: Synthetic Datasets Can Achieve Reliable Recognition Performance without Privacy Compromise
Paweł Borsukiewicz, Fadi Boutros, Iyiola E. Olatunji +4
The deployment of facial recognition systems has created an ethical dilemma: achieving high accuracy requires massive datasets of real faces collected without consent, leading to d…
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…
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…
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…