Marco Huber

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Vita

Marco Huber studied Computer Science from 2014 till 2021 at the Technical University of Darmstadt and graduated with a master’s degree in computer science with minor Entrepreneurship & Innovation and a master’s degree in Internet- and Webbased Systems.

From the beginning of 2019 to April 2021, he worked as a research assistant in the Smart Living & Biometric Technologies department of the Fraunhofer Institute for Computer Graphics Research IGD in Darmstadt. Since May 2021, he has been working there as a research associate.

His main research interests focus on machine learning, computer vision, and biometrics.

Publications

Jahr Year	Titel/Autor:in Title/Author	Publikationstyp Publication Type
2024	SFace2: Synthetic-Based Face Recognition with w-Space Identity-Driven Sampling Boutros, Fadi; Huber, Marco; Luu, Anh Thi; Siebke, Patrick; Damer, Naser	Zeitschriftenaufsatz Journal Article
2024	Bias and Diversity in Synthetic-based Face Recognition Huber, Marco; Luu, Anh Thi; Boutros, Fadi; Kuijper, Arjan; Damer, Naser	Konferenzbeitrag Conference Paper
2024	Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation Huber, Marco; Luu, Anh Thi; Terhörst, Philipp; Damer, Naser	Konferenzbeitrag Conference Paper
2023	Verfahren und System zur Personenverifikation in Porträtgemälden und Computerprogrammprodukt Terhörst, Philipp; Huber, Marco; Damer, Naser	Patent
2023	Uncertainty-aware Comparison Scores for Face Recognition Huber, Marco; Terhörst, Philipp; Kirchbuchner, Florian; Kuijper, Arjan; Damer, Naser	Konferenzbeitrag Conference Paper
2023	Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection Huber, Marco; Fang, Meiling; Boutros, Fadi; Damer, Naser	Konferenzbeitrag Conference Paper
2023	SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data Fang, Meiling; Huber, Marco; Damer, Naser	Paper
2023	MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders Damer, Naser; Fang, Meiling; Siebke, Patrick; Kolf, Jan Niklas; Huber, Marco; Boutros, Fadi	Konferenzbeitrag Conference Paper
2023	SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data Fang, Meiling; Huber, Marco; Damer, Naser	Konferenzbeitrag Conference Paper
2023	Pixel-Level Face Image Quality Assessment for Explainable Face Recognition Terhörst, Philipp; Huber, Marco; Damer, Naser; Kirchbuchner, Florian; Raja, Kiran; Kuijper, Arjan	Zeitschriftenaufsatz Journal Article
2023	Liveness Detection Competition - Noncontact-based Fingerprint Algorithms and Systems (LivDet-2023 Noncontact Fingerprint) Purnapatra, Sandip; Rezaie, Humaira; Jawade, Bhavin; Liu, Yu; Pan, Yue; Brosell, Luke; Sumi, Mst Rumana; Igene, Lambert; Dimarco, Alden; Setlur, Srirangaraj; Dey, Soumyabrata; Schuckers, Stephanie; Huber, Marco; Kolf, Jan Niklas; Fang, Meiling; Damer, Naser; Adami, Banafsheh; Chitic, Raul; Seelert, Karsten; Mistry, Vishesh; Parthe, Rahul; Kacar, Umit	Konferenzbeitrag Conference Paper
2023	SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data Fang, Meiling; Huber, Marco; Fierrez, Julian; Ramachandra, Raghavendra; Damer, Naser; Alkhaddour, Alhasan; Kasantcev, Maksim; Pryadchenko, Vasiliy; Yang, Ziyuan; Huangfu, Huijie; Chen, Yingyu; Zhang, Yi; Pan, Yuchen; Jiang, Junjun; Liu, Xianming; Sun, Xianyun; Wang, Caiyong; Liu, Xingyu; Chang, Zhaohua; Zhao, Guangzhe; Tapia, Juan; Gonzalez-Soler, Lazaro; Aravena, Carlos; Schulz, Daniel	Konferenzbeitrag Conference Paper
2023	QMagFace: Simple and Accurate Quality-Aware Face Recognition Terhörst, Philipp; Ihlefeld, Malte; Huber, Marco; Damer, Naser; Kirchbuchner, Florian; Raja, Kiran; Kuijper, Arjan	Konferenzbeitrag Conference Paper
2022	On Evaluating Pixel-Level Face Image Quality Assessment Huber, Marco; Terhörst, Philipp; Kirchbuchner, Florian; Damer, Naser; Kuijper, Arjan	Konferenzbeitrag Conference Paper
2022	Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition Huber, Marco; Terhörst, Philipp; Kirchbuchner, Florian; Damer, Naser; Kuijper, Arjan	Konferenzbeitrag Conference Paper
2022	OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement Neto, Pedro C.; Goncalves, Tiago; Huber, Marco; Damer, Naser; Sequeira, Ana F.; Cardoso, Jaime S.	Konferenzbeitrag Conference Paper
2022	On the (Limited) Generalization of MasterFace Attacks and its Relation to the Capacity of Face Representations Terhörst, Philipp; Bierbaum, Florian; Huber, Marco; Damer, Naser; Kirchbuchner, Florian; Raja, Kiran; Kuijper, Arjan	Konferenzbeitrag Conference Paper
2022	Verification of Sitter Identity Across Historical Portrait Paintings by Confidence-aware Face Recognition Huber, Marco; Terhörst, Philipp; Luu, Anh Thi; Damer, Naser; Kirchbuchner, Florian	Konferenzbeitrag Conference Paper
2022	SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data Boutros, Fadi; Huber, Marco; Siebke, Patrick; Rieber, Tim Jannik; Damer, Naser	Konferenzbeitrag Conference Paper
2022	A Comprehensive Study on Face Recognition Biases Beyond Demographics Terhörst, Philipp; Kolf, Jan Niklas; Huber, Marco; Kirchbuchner, Florian; Damer, Naser; Morales, Aythami; Fierrez, Julian; Kuijper, Arjan	Zeitschriftenaufsatz Journal Article
2022	SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data Huber, Marco; Boutros, Fadi; Luu, Anh Thi; Kiran, Raja; Ramachandra, Raghavendra; Damer, Naser; Neto, Pedro C.; Goncalves, Tiago J.; Sequeira, Ana F.; Cardoso, Jaime S.; Tremoco, Joao; Lourenco, Miguel; Serra, Sergio; Cermeno, Eduardo; Ivanovska, Marija; Batagelj, Borut; Kronovšek, Andrej; Peer, Peter; Štruc, Vitomir	Konferenzbeitrag Conference Paper
2021	Explainable Face Image Quality Assessment Huber, Marco	Master Thesis
2021	Mask-invariant Face Recognition through Template-level Knowledge Distillation Huber, Marco; Boutros, Fadi; Kirchbuchner, Florian; Damer, Naser	Konferenzbeitrag Conference Paper
2020	Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies Terhörst, Philipp; Huber, Marco; Damer, Naser; Rot, Peter; Kirchbuchner, Florian; Struc, Vitomir; Kuijper, Arjan	Konferenzbeitrag Conference Paper
2019	Multi-algorithmic Fusion for Reliable Age and Gender Estimation from Face Images Terhörst, Philipp; Huber, Marco; Kolf, Jan Niklas; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan	Konferenzbeitrag Conference Paper
2019	Reliable Age and Gender Estimation from Face Images: Stating the Confidence of Model Predictions Terhörst, Philipp; Zelch, Ines; Huber, Marco; Kolf, Jan Niklas; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan	Konferenzbeitrag Conference Paper

Diese Liste ist ein Auszug aus der Publikationsplattform Fraunhofer-Publica

This list has been generated from the publication platform Fraunhofer-Publica

Projects

ATHENE - Mission “Next Generation Biometric System” – Project “Identity Management”

As part of the National Research Center for Applied Cybersecurity, we develop biometric solutions in the context of identity management. This includes the detection and prevention of attacks on biometric systems (e.g., Presentation Attacks and Morphing Attacks), the reduction of discriminatory behavior of such systems, and the quality assessment of biometric samples. The goal of this project is to make biometric systems safer, fairer, and more reliable for society.

ATHENE Research Area Next Generation Biometric Systems (NGBS)
Technology Face morphing
Face morphing: a new threat?

PEG – Privacy-Preserving Face Recognition

In conventional facial recognition systems, significantly more information is indirectly processed and stored than is needed for the actual task of identity verification. This information is often privacy-sensitive and must not be processed or stored without prior consent. In this project, we have successfully developed methods to remove this private information from the system in order to allow a higher standard of privacy protection.

IIG –Identification in Paintings

The aim of this project is to unequivocally identify persons depicted in paintings by software specifically developed for this purpose. Modern face recognition systems meet their limits here, since the painted persons are often not perfectly represented and there is a change of domains. The aim of this project is to unambiguously determine the identity of the persons depicted in the paintings in order to be able to prove historically relevant connections.