17 citations · 36 across the 13 of their papers we have counts for
20 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…
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…
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…
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…
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…
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…
GraFIQs: Face Image Quality Assessment Using Gradient Magnitudes
Jan Niklas Kolf, Naser Damer, Fadi Boutros
Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems. We propose in this work a novel approach to assess the qualit…
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…
Liveness Detection Competition -- Noncontact-based Fingerprint Algorithms and Systems (LivDet-2023 Noncontact Fingerprint)
Sandip Purnapatra, Humaira Rezaie, Bhavin Jawade +19
Liveness Detection (LivDet) is an international competition series open to academia and industry with the objec-tive to assess and report state-of-the-art in Presentation Attack De…
EFaR 2023: Efficient Face Recognition Competition
Jan Niklas Kolf, Fadi Boutros, Jurek Elliesen +24
This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition rece…
Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition
Jan Niklas Kolf, Tim Rieber, Jurek Elliesen +3
Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the socia…
MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders
Naser Damer, Meiling Fang, Patrick Siebke +3
Investigating new methods of creating face morphing attacks is essential to foresee novel attacks and help mitigate them. Creating morphing attacks is commonly either performed on…
MFR 2021: Masked Face Recognition Competition
Fadi Boutros, Naser Damer, Jan Niklas Kolf +32
This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attra…
A Comprehensive Study on Face Recognition Biases Beyond Demographics
Philipp Terhörst, Jan Niklas Kolf, Marco Huber +5
Face recognition (FR) systems have a growing effect on critical decision-making processes. Recent works have shown that FR solutions show strong performance differences based on th…
MAAD-Face: A Massively Annotated Attribute Dataset for Face Images
Philipp Terhörst, Daniel Fährmann, Jan Niklas Kolf +3
Soft-biometrics play an important role in face biometrics and related fields since these might lead to biased performances, threatens the user's privacy, or are valuable for commer…
Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition
Philipp Terhörst, Jan Niklas Kolf, Naser Damer +2
Face quality assessment aims at estimating the utility of a face image for the purpose of recognition. It is a key factor to achieve high face recognition performances. Currently,…
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
Philipp Terhörst, Jan Niklas Kolf, Naser Damer +2
Face image quality is an important factor to enable high performance face recognition systems. Face quality assessment aims at estimating the suitability of a face image for recogn…
Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization
Philipp Terhörst, Jan Niklas Kolf, Naser Damer +2
Current face recognition systems achieve high progress on several benchmark tests. Despite this progress, recent works showed that these systems are strongly biased against demogra…