no code implementations • 19 Apr 2024 • Benjamin Fresz, Elena Dubovitskaya, Danilo Brajovic, Marco Huber, Christian Horz
This paper investigates the relationship between law and eXplainable Artificial Intelligence (XAI).
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
2 code implementations • 16 Apr 2024 • Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi, Ruben Vera-Rodriguez, Minchul Kim, Christian Rathgeb, Xiaoming Liu, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia, Zhizhou Zhong, Yuge Huang, Yuxi Mi, Shouhong Ding, Shuigeng Zhou, Shuai He, Lingzhi Fu, Heng Cong, Rongyu Zhang, Zhihong Xiao, Evgeny Smirnov, Anton Pimenov, Aleksei Grigorev, Denis Timoshenko, Kaleb Mesfin Asfaw, Cheng Yaw Low, Hao liu, Chuyi Wang, Qing Zuo, Zhixiang He, Hatef Otroshi Shahreza, Anjith George, Alexander Unnervik, Parsa Rahimi, Sébastien Marcel, Pedro C. Neto, Marco Huber, Jan Niklas Kolf, Naser Damer, Fadi Boutros, Jaime S. Cardoso, Ana F. Sequeira, Andrea Atzori, Gianni Fenu, Mirko Marras, Vitomir Štruc, Jiang Yu, Zhangjie Li, Jichun Li, Weisong Zhao, Zhen Lei, Xiangyu Zhu, Xiao-Yu Zhang, Bernardo Biesseck, Pedro Vidal, Luiz Coelho, Roger Granada, David Menotti
Synthetic data is gaining increasing relevance for training machine learning models.
1 code implementation • 18 Jan 2024 • Eduarda Caldeira, Pedro C. Neto, Marco Huber, Naser Damer, Ana F. Sequeira
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity.
1 code implementation • 9 Nov 2023 • Meiling Fang, Marco Huber, Julian Fierrez, Raghavendra Ramachandra, Naser Damer, Alhasan Alkhaddour, Maksim Kasantcev, Vasiliy Pryadchenko, Ziyuan Yang, Huijie Huangfu, Yingyu Chen, Yi Zhang, Yuchen Pan, Junjun Jiang, Xianming Liu, Xianyun Sun, Caiyong Wang, Xingyu Liu, Zhaohua Chang, Guangzhe Zhao, Juan Tapia, Lazaro Gonzalez-Soler, Carlos Aravena, Daniel Schulz
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 International Joint Conference on Biometrics (IJCB 2023).
no code implementations • 7 Nov 2023 • Marco Huber, Anh Thi Luu, Fadi Boutros, Arjan Kuijper, Naser Damer
In this work, we investigate how the diversity of synthetic face recognition datasets compares to authentic datasets, and how the distribution of the training data of the generative models affects the distribution of the synthetic data.
no code implementations • 1 Oct 2023 • Sandip Purnapatra, Humaira Rezaie, Bhavin Jawade, Yu Liu, Yue Pan, Luke Brosell, Mst Rumana Sumi, Lambert Igene, Alden Dimarco, Srirangaraj Setlur, Soumyabrata Dey, Stephanie Schuckers, Marco Huber, Jan Niklas Kolf, Meiling Fang, Naser Damer, Banafsheh Adami, Raul Chitic, Karsten Seelert, Vishesh Mistry, Rahul Parthe, Umit Kacar
The competition serves as an important benchmark in noncontact-based fingerprint PAD, offering (a) independent assessment of the state-of-the-art in noncontact-based fingerprint PAD for algorithms and systems, and (b) common evaluation protocol, which includes finger photos of a variety of Presentation Attack Instruments (PAIs) and live fingers to the biometric research community (c) provides standard algorithm and system evaluation protocols, along with the comparative analysis of state-of-the-art algorithms from academia and industry with both old and new android smartphones.
no code implementations • 26 Apr 2023 • Marco Huber, Meiling Fang, Fadi Boutros, Naser Damer
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 deep learning.
1 code implementation • 26 Apr 2023 • Marco Huber, Anh Thi Luu, Philipp Terhörst, Naser Damer
Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications.
1 code implementation • 5 Mar 2023 • Meiling Fang, Marco Huber, Naser Damer
To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset.
1 code implementation • 3 Feb 2023 • Naser Damer, Meiling Fang, Patrick Siebke, Jan Niklas Kolf, Marco Huber, Fadi Boutros
Creating morphing attacks is commonly either performed on the image-level or on the representation-level.
no code implementations • 19 Oct 2022 • Marco Huber, Philipp Terhörst, Florian Kirchbuchner, Naser Damer, Arjan Kuijper
The confidence of a decision is often based on the overall performance of the model or on the image quality.
1 code implementation • 16 Aug 2022 • Pedro C. Neto, Tiago Gonçalves, Marco Huber, Naser Damer, Ana F. Sequeira, Jaime S. Cardoso
Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems.
1 code implementation • 15 Aug 2022 • Marco Huber, Fadi Boutros, Anh Thi Luu, Kiran Raja, Raghavendra Ramachandra, Naser Damer, Pedro C. Neto, Tiago Gonçalves, Ana F. Sequeira, Jaime S. Cardoso, João Tremoço, Miguel Lourenço, Sergio Serra, Eduardo Cermeño, Marija Ivanovska, Borut Batagelj, Andrej Kronovšek, Peter Peer, Vitomir Štruc
The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries.
1 code implementation • 21 Jun 2022 • Fadi Boutros, Marco Huber, Patrick Siebke, Tim Rieber, Naser Damer
The reported evaluation results on five authentic face benchmarks demonstrated that the privacy-friendly synthetic dataset has high potential to be used for training face recognition models, achieving, for example, a verification accuracy of 91. 87\% on LFW using multi-class classification and 99. 13\% using the combined learning strategy.
no code implementations • 23 Mar 2022 • Philipp Terhörst, Florian Bierbaum, Marco Huber, Naser Damer, Florian Kirchbuchner, Kiran Raja, Arjan Kuijper
However, previous works followed evaluation settings consisting of older recognition models, limited cross-dataset and cross-model evaluations, and the use of low-scale testing data.
no code implementations • 28 Jan 2022 • Raoul Schönhof, Artem Werner, Jannes Elstner, Boldizsar Zopcsak, Ramez Awad, Marco Huber
Explainable AI methods have been used in order to assess whether a neural network has successfully learned a given task or to analyze which features of an input might lead to an adversarial attack.
1 code implementation • 10 Dec 2021 • Marco Huber, Fadi Boutros, Florian Kirchbuchner, Naser Damer
The emergence of the global COVID-19 pandemic poses new challenges for biometrics.
1 code implementation • 26 Nov 2021 • Philipp Terhörst, Malte Ihlefeld, Marco Huber, Naser Damer, Florian Kirchbuchner, Kiran Raja, Arjan Kuijper
These variabilities can be measured in terms of face image quality which is defined over the utility of a sample for recognition.
Ranked #1 on Face Verification on IJB-B
1 code implementation • 21 Oct 2021 • Philipp Terhörst, Marco Huber, Naser Damer, Florian Kirchbuchner, Kiran Raja, Arjan Kuijper
To achieve this, a model-specific quality value of the input image is estimated and used to build a sample-specific quality regression model.
no code implementations • 29 Sep 2021 • Danilo Brajovic, Omar de Mitri, Alex Windberger, Marco Huber
Understanding the influence of data on machine learning models is an emerging research field.
no code implementations • 2 Mar 2021 • Philipp Terhörst, Jan Niklas Kolf, Marco Huber, Florian Kirchbuchner, Naser Damer, Aythami Morales, Julian Fierrez, Arjan Kuijper
However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics.
1 code implementation • 21 Feb 2020 • Philipp Terhörst, Marco Huber, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
Current research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric templates of an individual.