no code implementations • 31 Oct 2024 • Tahar Chettaoui, Naser Damer, Fadi Boutros
Even with access to large-scale face recognition training datasets, fine-tuned foundation models perform comparably to models trained from scratch, but with lower training computational costs and without relying on the assumption of extensive data availability.
no code implementations • 23 Aug 2024 • Biying Fu, Fadi Boutros, Chin-Teng Lin, Naser Damer
By examining the multifaceted implications of drowsiness, this work contributes to a holistic understanding of its impact and the crucial role of accurate and real-time detection techniques in enhancing safety and performance.
no code implementations • 16 Jul 2024 • Marco Huber, Naser Damer
In this work, we take a step forward and investigate explainable face recognition in the unexplored frequency domain.
1 code implementation • 1 Jul 2024 • Fadi Boutros, Vitomir Štruc, Naser Damer
Knowledge distillation (KD) aims at improving the performance of a compact student model by distilling the knowledge from a high-performing teacher model.
1 code implementation • 18 Apr 2024 • Jan Niklas Kolf, Naser Damer, Fadi Boutros
We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset.
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 • 15 Apr 2024 • Žiga Babnik, Fadi Boutros, Naser Damer, Peter Peer, Vitomir Štruc
To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures.
no code implementations • 6 Apr 2024 • Hatef Otroshi Shahreza, Christophe Ecabert, Anjith George, Alexander Unnervik, Sébastien Marcel, Nicolò Di Domenico, Guido Borghi, Davide Maltoni, Fadi Boutros, Julia Vogel, Naser Damer, Ángela Sánchez-Pérez, EnriqueMas-Candela, Jorge Calvo-Zaragoza, Bernardo Biesseck, Pedro Vidal, Roger Granada, David Menotti, Ivan DeAndres-Tame, Simone Maurizio La Cava, Sara Concas, Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez, Gianpaolo Perelli, Giulia Orrù, Gian Luca Marcialis, Julian Fierrez
The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data.
no code implementations • 4 Apr 2024 • Andrea Atzori, Fadi Boutros, Naser Damer, Gianni Fenu, Mirko Marras
Finally, we assessed the effectiveness of data augmentation approaches on synthetic and authentic data, with the same goal in mind.
1 code implementation • 29 Jan 2024 • Giuseppe Stragapede, Ruben Vera-Rodriguez, Ruben Tolosana, Aythami Morales, Ivan DeAndres-Tame, Naser Damer, Julian Fierrez, Javier-Ortega Garcia, Nahuel Gonzalez, Andrei Shadrikov, Dmitrii Gordin, Leon Schmitt, Daniel Wimmer, Christoph Grossmann, Joerdis Krieger, Florian Heinz, Ron Krestel, Christoffer Mayer, Simon Haberl, Helena Gschrey, Yosuke Yamagishi, Sanjay Saha, Sanka Rasnayaka, Sandareka Wickramanayake, Terence Sim, Weronika Gutfeter, Adam Baran, Mateusz Krzyszton, Przemyslaw Jaskola
Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3. 33% and 3. 61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD.
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.
no code implementations • 20 Nov 2023 • Biying Fu, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
In previous works, a mobile application was developed using an unmodified commercial off-the-shelf smartphone to recognize whole-body exercises.
2 code implementations • 17 Nov 2023 • Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez, Minchul Kim, Christian Rathgeb, Xiaoming Liu, Ivan DeAndres-Tame, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia, Weisong Zhao, Xiangyu Zhu, Zheyu Yan, Xiao-Yu Zhang, Jinlin Wu, Zhen Lei, Suvidha Tripathi, Mahak Kothari, Md Haider Zama, Debayan Deb, Bernardo Biesseck, Pedro Vidal, Roger Granada, Guilherme Fickel, Gustavo Führ, David Menotti, Alexander Unnervik, Anjith George, Christophe Ecabert, Hatef Otroshi Shahreza, Parsa Rahimi, Sébastien Marcel, Ioannis Sarridis, Christos Koutlis, Georgia Baltsou, Symeon Papadopoulos, Christos Diou, Nicolò Di Domenico, Guido Borghi, Lorenzo Pellegrini, Enrique Mas-Candela, Ángela Sánchez-Pérez, Andrea Atzori, Fadi Boutros, Naser Damer, Gianni Fenu, Mirko Marras
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
no code implementations • 10 Nov 2023 • Giuseppe Stragapede, Ruben Vera-Rodriguez, Ruben Tolosana, Aythami Morales, Naser Damer, Julian Fierrez, Javier Ortega-Garcia
Analyzing keystroke dynamics (KD) for biometric verification has several advantages: it is among the most discriminative behavioral traits; keyboards are among the most common human-computer interfaces, being the primary means for users to enter textual data; its acquisition does not require additional hardware, and its processing is relatively lightweight; and it allows for transparently recognizing subjects.
2 code implementations • 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 • 6 Oct 2023 • Patrick Tinsley, Sandip Purnapatra, Mahsa Mitcheff, Aidan Boyd, Colton Crum, Kevin Bowyer, Patrick Flynn, Stephanie Schuckers, Adam Czajka, Meiling Fang, Naser Damer, Xingyu Liu, Caiyong Wang, Xianyun Sun, Zhaohua Chang, Xinyue Li, Guangzhe Zhao, Juan Tapia, Christoph Busch, Carlos Aravena, Daniel Schulz
New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark.
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.
1 code implementation • 28 Aug 2023 • Meiling Fang, Naser Damer
We excavate the causal factors hidden in the high-level representation via counterfactual intervention.
1 code implementation • 9 Aug 2023 • Fadi Boutros, Jonas Henry Grebe, Arjan Kuijper, Naser Damer
The availability of large-scale authentic face databases has been crucial to the significant advances made in face recognition research over the past decade.
Ranked #2 on Synthetic Face Recognition on AgeDB-30 (Accuracy metric)
1 code implementation • 8 Aug 2023 • Jan Niklas Kolf, Fadi Boutros, Jurek Elliesen, Markus Theuerkauf, Naser Damer, Mohamad Alansari, Oussama Abdul Hay, Sara Alansari, Sajid Javed, Naoufel Werghi, Klemen Grm, Vitomir Štruc, Fernando Alonso-Fernandez, Kevin Hernandez Diaz, Josef Bigun, Anjith George, Christophe Ecabert, Hatef Otroshi Shahreza, Ketan Kotwal, Sébastien Marcel, Iurii Medvedev, Bo Jin, Diogo Nunes, Ahmad Hassanpour, Pankaj Khatiwada, Aafan Ahmad Toor, Bian Yang
To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size.
1 code implementation • 11 Jul 2023 • Fadi Boutros, Marcel Klemt, Meiling Fang, Arjan Kuijper, Naser Damer
To generate multiple samples of a certain synthetic identity, previous works proposed to disentangle the latent space of GANs by incorporating additional supervision or regularization, enabling the manipulation of certain attributes.
1 code implementation • 5 Jun 2023 • Eduarda Caldeira, Pedro C. Neto, Tiago Gonçalves, Naser Damer, Ana F. Sequeira, Jaime S. Cardoso
Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown.
Ranked #1 on Face Morphing Attack Detection on FRLL-Morphs
1 code implementation • 24 May 2023 • Žiga Babnik, Naser Damer, Vitomir Štruc
To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality information from the input face images that can aid with the recognition process.
no code implementations • 1 May 2023 • Fadi Boutros, Vitomir Struc, Julian Fierrez, Naser Damer
Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy.
2 code implementations • 30 Apr 2023 • Jan Niklas Kolf, Tim Rieber, Jurek Elliesen, Fadi Boutros, Arjan Kuijper, Naser Damer
We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation.
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.
no code implementations • 7 Apr 2023 • Raghavendra Ramachandra, Sushma Venkatesh, Naser Damer, Narayan Vetrekar, Rajendra Gad
The D-MAD methods are based on using two facial images that are captured from the ePassport (also called the reference image) and the trusted device (for example, Automatic Border Control (ABC) gates) to detect whether the face image presented in ePassport is morphed.
2 code implementations • 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.
1 code implementation • ICCV 2023 • Fadi Boutros, Jonas Henry Grebe, Arjan Kuijper, Naser Damer
The availability of large-scale authentic face databases has been crucial to the significant advances made in face recognition research over the past decade.
no code implementations • 28 Dec 2022 • Fernando Alonso-Fernandez, Josef Bigun, Julian Fierrez, Naser Damer, Hugo Proença, Arun Ross
Periocular refers to the externally visible region of the face that surrounds the eye socket.
1 code implementation • 14 Nov 2022 • Fadi Boutros, Marcel Klemt, Meiling Fang, Arjan Kuijper, Naser Damer
In this paper, we propose an unsupervised face recognition model based on unlabeled synthetic data (USynthFace).
Ranked #1 on Unsupervised face recognition on LFW
no code implementations • 7 Nov 2022 • Fevziye Irem Eyiokur, Alperen Kantarcı, Mustafa Ekrem Erakin, Naser Damer, Ferda Ofli, Muhammad Imran, Janez Križaj, Albert Ali Salah, Alexander Waibel, Vitomir Štruc, Hazim Kemal Ekenel
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals.
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.
2 code implementations • 19 Sep 2022 • Meiling Fang, Wufei Yang, Arjan Kuijper, Vitomir Struc, Naser Damer
Face recognition (FR) algorithms have been proven to exhibit discriminatory behaviors against certain demographic and non-demographic groups, raising ethical and legal concerns regarding their deployment in real-world scenarios.
1 code implementation • 29 Aug 2022 • Biying Fu, Naser Damer
To improve the trustfulness of such ML decision systems, it is crucial to be aware of the inherent biases in these solutions and to make them more transparent to the public and developers.
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.
no code implementations • 11 Aug 2022 • Biying Fu, Naser Damer
A morphed image can be successfully verified to multiple identities.
1 code implementation • 11 Aug 2022 • Meiling Fang, Fadi Boutros, Naser Damer
However, given variations in the morphing attacks, the performance of supervised MAD solutions drops significantly due to the insufficient diversity and quantity of the existing MAD datasets.
1 code implementation • 4 Aug 2022 • Pedro C. Neto, Fadi Boutros, Joao Ribeiro Pinto, Naser Damer, Ana F. Sequeira, Jaime S. Cardoso, Messaoud Bengherabi, Abderaouf Bousnat, Sana Boucheta, Nesrine Hebbadj, Mustafa Ekrem Erakin, Uğur Demir, Hazim Kemal Ekenel, Pedro Beber de Queiroz Vidal, David Menotti
This work summarizes the IJCB Occluded Face Recognition Competition 2022 (IJCB-OCFR-2022) embraced by the 2022 International Joint Conference on Biometrics (IJCB 2022).
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.
1 code implementation • 21 Jun 2022 • Fadi Boutros, Naser Damer, Arjan Kuijper
Deep learning-based face recognition models follow the common trend in deep neural networks by utilizing full-precision floating-point networks with high computational costs.
Ranked #1 on Quantization on LFW
no code implementations • 5 May 2022 • Meiling Fang, Fadi Boutros, Naser Damer
Extensive experiments are performed on six NIR and one visible-light iris databases to show the effectiveness and robustness of proposed A-PBS methods.
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.
1 code implementation • 13 Mar 2022 • Naser Damer, César Augusto Fontanillo López, Meiling Fang, Noémie Spiller, Minh Vu Pham, Fadi Boutros
The main question this work aims at answering is: "can morphing attack detection (MAD) solutions be successfully developed based on synthetic data?".
1 code implementation • CVPR 2023 • Fadi Boutros, Meiling Fang, Marcel Klemt, Biying Fu, Naser Damer
Based on that, our proposed CR-FIQA uses this paradigm to estimate the face image quality of a sample by predicting its relative classifiability.
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 • 9 Dec 2021 • Biying Fu, Naser Damer
To avoid the low discrimination between the general spatial activation mapping of low and high-quality images in FR models, we build our explainability tools in a higher derivative space by analyzing the variation of the FR activation maps of image sets with different quality decisions.
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
no code implementations • 8 Nov 2021 • Meiling Fang, Fadi Boutros, Arjan Kuijper, Naser Damer
Our proposed method outperforms established PAD methods in the CRMA database by reducing the mentioned shortcomings when facing masked faces.
1 code implementation • 28 Oct 2021 • Pedro C. Neto, Fadi Boutros, João Ribeiro Pinto, Naser Damer, Ana F. Sequeira, Jaime S. Cardoso
The proposed architecture is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of a existing models in conventional face recognition tasks.
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 • 21 Oct 2021 • Biying Fu, Cong Chen, Olaf Henniger, Naser Damer
This paper focuses on face images and the measurement of face image utility with general and face-specific image quality metrics.
no code implementations • 21 Oct 2021 • Biying Fu, Florian Kirchbuchner, Naser Damer
This work studies, for the first time, the effect of wearing a face mask on face image quality by utilising state-of-the-art face image quality assessment methods of different natures.
3 code implementations • 20 Sep 2021 • Fadi Boutros, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
The recent state-of-the-art face recognition solutions proposed to incorporate a fixed penalty margin on commonly used classification loss function, softmax loss, in the normalized hypersphere to increase the discriminative power of face recognition models, by minimizing the intra-class variation and maximizing the inter-class variation.
Ranked #1 on Face Recognition on IJB-B (TAR @ FAR=0.0001 metric)
2 code implementations • 16 Sep 2021 • Meiling Fang, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
With the increased deployment of face recognition systems in our daily lives, face presentation attack detection (PAD) is attracting much attention and playing a key role in securing face recognition systems.
1 code implementation • 24 Aug 2021 • Fadi Boutros, Patrick Siebke, Marcel Klemt, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
However, this limits the deployment of such models that contain an extremely large number of parameters to embedded and low-end devices.
Ranked #2 on Lightweight Face Recognition on CALFW
no code implementations • 23 Aug 2021 • Naser Damer, Noemie Spiller, Meiling Fang, Fadi Boutros, Florian Kirchbuchner, Arjan Kuijper
A face morphing attack image can be verified to multiple identities, making this attack a major vulnerability to processes based on identity verification, such as border checks.
no code implementations • 20 Aug 2021 • Naser Damer, Kiran Raja, Marius Süßmilch, Sushma Venkatesh, Fadi Boutros, Meiling Fang, Florian Kirchbuchner, Raghavendra Ramachandra, Arjan Kuijper
Face morphing attacks aim at creating face images that are verifiable to be the face of multiple identities, which can lead to building faulty identity links in operations like border checks.
no code implementations • 2 Aug 2021 • Pedro C. Neto, Fadi Boutros, João Ribeiro Pinto, Mohsen Saffari, Naser Damer, Ana F. Sequeira, Jaime S. Cardoso
The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS).
1 code implementation • 27 Jul 2021 • Fadi Boutros, Naser Damer, Meiling Fang, Florian Kirchbuchner, Arjan Kuijper
In this paper, we present a set of extremely efficient and high throughput models for accurate face verification, MixFaceNets which are inspired by Mixed Depthwise Convolutional Kernels.
Ranked #3 on Lightweight Face Recognition on IJB-C
no code implementations • 29 Jun 2021 • Fadi Boutros, Naser Damer, Jan Niklas Kolf, Kiran Raja, Florian Kirchbuchner, Raghavendra Ramachandra, Arjan Kuijper, Pengcheng Fang, Chao Zhang, Fei Wang, David Montero, Naiara Aginako, Basilio Sierra, Marcos Nieto, Mustafa Ekrem Erakin, Ugur Demir, Hazim Kemal, Ekenel, Asaki Kataoka, Kohei Ichikawa, Shizuma Kubo, Jie Zhang, Mingjie He, Dan Han, Shiguang Shan, Klemen Grm, Vitomir Štruc, Sachith Seneviratne, Nuran Kasthuriarachchi, Sanka Rasnayaka, Pedro C. Neto, Ana F. Sequeira, Joao Ribeiro Pinto, Mohsen Saffari, Jaime S. Cardoso
These teams successfully submitted 18 valid solutions.
no code implementations • 28 Jun 2021 • Meiling Fang, Naser Damer, Fadi Boutros, Florian Kirchbuchner, Arjan Kuijper
Iris presentation attack detection (PAD) plays a vital role in iris recognition systems.
1 code implementation • 10 Jun 2021 • Philipp Terhörst, André Boller, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
The implementation is publicly available.
no code implementations • 31 May 2021 • Christian Rathgeb, Pawel Drozdowski, Naser Damer, Dinusha C. Frings, Christoph Busch
Algorithmic decision systems have frequently been labelled as "biased", "racist", "sexist", or "unfair" by numerous media outlets, organisations, and researchers.
no code implementations • 2 Mar 2021 • Naser Damer, Fadi Boutros, Marius Süßmilch, Meiling Fang, Florian Kirchbuchner, Arjan Kuijper
This work provides a joint evaluation and in-depth analyses of the face verification performance of human experts in comparison to state-of-the-art automatic FR solutions.
no code implementations • 2 Mar 2021 • Meiling Fang, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
Face masks have become one of the main methods for reducing the transmission of COVID-19.
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 • 2 Mar 2021 • Fadi Boutros, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
Using the face as a biometric identity trait is motivated by the contactless nature of the capture process and the high accuracy of the recognition algorithms.
no code implementations • 18 Feb 2021 • Marta Gomez-Barrero, Pawel Drozdowski, Christian Rathgeb, Jose Patino, Massimmiliano Todisco, Andras Nautsch, Naser Damer, Jannis Priesnitz, Nicholas Evans, Christoph Busch
Since early 2020 the COVID-19 pandemic has had a considerable impact on many aspects of daily life.
1 code implementation • 2 Dec 2020 • Philipp Terhörst, Daniel Fährmann, Jan Niklas Kolf, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
In this work, we propose MAADFace, a new face annotations database that is characterized by the large number of its high-quality attribute annotations.
no code implementations • 28 Oct 2020 • Meiling Fang, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
In this paper, we propose a lightweight framework to detect iris presentation attacks by extracting multiple micro-stripes of expanded normalized iris textures.
no code implementations • 20 Oct 2020 • Fadi Boutros, Naser Damer, Kiran Raja, Raghavendra Ramachandra, Florian Kirchbuchner, Arjan Kuijper
Motivated by the performance of iris recognition, we also propose the continuous authentication of users in a non-collaborative capture setting in HMD.
no code implementations • 21 Sep 2020 • Philipp Terhörst, Daniel Fährmann, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
For evaluating the predictability of the attributes, we trained a massive attribute classifier that is additionally able to accurately state its prediction confidence.
no code implementations • 3 Sep 2020 • Haoyu Zhang, Sushma Venkatesh, Raghavendra Ramachandra, Kiran Raja, Naser Damer, Christoph Busch
Extensive experiments are carried out to assess the FRS's vulnerability against the proposed morphed face generation technique on three types of data such as digital images, re-digitized (printed and scanned) images, and compressed images after re-digitization from newly generated MIPGAN Face Morph Dataset.
no code implementations • 1 Sep 2020 • Priyanka Das, Joseph McGrath, Zhaoyuan Fang, Aidan Boyd, Ganghee Jang, Amir Mohammadi, Sandip Purnapatra, David Yambay, Sébastien Marcel, Mateusz Trokielewicz, Piotr Maciejewicz, Kevin Bowyer, Adam Czajka, Stephanie Schuckers, Juan Tapia, Sebastian Gonzalez, Meiling Fang, Naser Damer, Fadi Boutros, Arjan Kuijper, Renu Sharma, Cunjian Chen, Arun Ross
Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD).
no code implementations • 27 Jul 2020 • Naser Damer, Jonas Henry Grebe, Cong Chen, Fadi Boutros, Florian Kirchbuchner, Arjan Kuijper
The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification.
no code implementations • 7 Jul 2020 • Sushma Venkatesh, Haoyu Zhang, Raghavendra Ramachandra, Kiran Raja, Naser Damer, Christoph Busch
\textit{(i) Can GAN generated morphs threaten Face Recognition Systems (FRS) equally as Landmark based morphs?}
1 code implementation • 2 Apr 2020 • Philipp Terhörst, Jan Niklas Kolf, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
Face quality assessment aims at estimating the utility of a face image for the purpose of recognition.
5 code implementations • 20 Mar 2020 • Philipp Terhörst, Jan Niklas Kolf, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
Face image quality is an important factor to enable high performance face recognition systems.
Ranked #1 on Face Quality Assessement on LFW
no code implementations • 6 Mar 2020 • Meiling Fang, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
With the widespread use of biometric systems, the demographic bias problem raises more attention.
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.
1 code implementation • 10 Feb 2020 • Philipp Terhörst, Jan Niklas Kolf, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
In contrast to previous works, our fair normalization approach enhances the overall performance by up to 53. 2% at false match rate of 0. 001 and up to 82. 9% at a false match rate of 0. 00001.
no code implementations • 21 Oct 2019 • Naser Damer, Fadi Boutros, Khawla Mallat, Florian Kirchbuchner, Jean-Luc Dugelay, Arjan Kuijper
Generating visible-like face images from thermal images is essential to perform manual and automatic cross-spectrum face recognition.