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 • 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 • 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.
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 • 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 • 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
1 code implementation • 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 • 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.
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.
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.
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 • 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.
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.
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 • 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.
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 • 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 • 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 • 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 • 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.