18 citations · 72 across the 26 of their papers we have counts for
27 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…
ReCowGnition: A Realistic Biometric Benchmark for Cow Face Recognition
Marco Huber, Marco Kiesewalter, Judith Louise Pieper +2
With the development of precision livestock farming and the advances in computer vision, visual animal biometrics has gained attention. Using biometric technologies that have been…
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…
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…
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…
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…
Beyond Spatial Explanations: Explainable Face Recognition in the Frequency Domain
Marco Huber, Naser Damer
The need for more transparent face recognition (FR), along with other visual-based decision-making systems has recently attracted more attention in research, society, and industry.…
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…
Model Compression Techniques in Biometrics Applications: A Survey
Eduarda Caldeira, Pedro C. Neto, Marco Huber +2
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is h…
SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data
Meiling Fang, Marco Huber, Julian Fierrez +21
This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 Internation…
Bias and Diversity in Synthetic-based Face Recognition
Marco Huber, Anh Thi Luu, Fadi Boutros +2
Synthetic data is emerging as a substitute for authentic data to solve ethical and legal challenges in handling authentic face data. The current models can create real-looking face…
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…
Self-supervised Optimization of Hand Pose Estimation using Anatomical Features and Iterative Learning
Christian Jauch, Timo Leitritz, Marco F. Huber
Manual assembly workers face increasing complexity in their work. Human-centered assistance systems could help, but object recognition as an enabling technology hinders sophisticat…
SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data
Meiling Fang, Marco Huber, Naser Damer
Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the a…
Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection
Marco Huber, Meiling Fang, Fadi Boutros +1
Face recognition (FR) systems continue to spread in our daily lives with an increasing demand for higher explainability and interpretability of FR systems that are mainly based on…
Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation
Marco Huber, Anh Thi Luu, Philipp Terhörst +1
Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications. Understanding why two faces images are m…
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…
Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition
Marco Huber, Philipp Terhörst, Florian Kirchbuchner +2
Face Recognition (FR) is increasingly used in critical verification decisions and thus, there is a need for assessing the trustworthiness of such decisions. The confidence of a dec…
OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
Pedro C. Neto, Tiago Gonçalves, Marco Huber +3
Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems. It consists of selecting two faces from different individuals and fusing t…
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data
Marco Huber, Fadi Boutros, Anh Thi Luu +16
This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Con…
SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data
Fadi Boutros, Marco Huber, Patrick Siebke +2
Recent deep face recognition models proposed in the literature utilized large-scale public datasets such as MS-Celeb-1M and VGGFace2 for training very deep neural networks, achievi…
On the (Limited) Generalization of MasterFace Attacks and Its Relation to the Capacity of Face Representations
Philipp Terhörst, Florian Bierbaum, Marco Huber +4
A MasterFace is a face image that can successfully match against a large portion of the population. Since their generation does not require access to the information of the enrolle…
Mask-invariant Face Recognition through Template-level Knowledge Distillation
Marco Huber, Fadi Boutros, Florian Kirchbuchner +1
The emergence of the global COVID-19 pandemic poses new challenges for biometrics. Not only are contactless biometric identification options becoming more important, but face recog…
Pixel-Level Face Image Quality Assessment for Explainable Face Recognition
Philipp Terhörst, Marco Huber, Naser Damer +3
An essential factor to achieve high performance in face recognition systems is the quality of its samples. Since these systems are involved in daily life there is a strong need of…