M
Meiling Fang
Researcher at Fraunhofer Society
Publications - 35
Citations - 345
Meiling Fang is an academic researcher from Fraunhofer Society. The author has contributed to research in topics: Computer science & Iris recognition. The author has an hindex of 6, co-authored 23 publications receiving 84 citations. Previous affiliations of Meiling Fang include Technische Universität Darmstadt.
Papers
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Proceedings ArticleDOI
MixFaceNets: Extremely Efficient Face Recognition Networks
TL;DR: MixFaceNets as discussed by the authors is a set of extremely efficient and high throughput models for accurate face verification, which are inspired by Mixed Depthwise Convolutional Kernels (MDCK).
Proceedings ArticleDOI
Iris Liveness Detection Competition (LivDet-Iris) - The 2020 Edition
Priyanka Das,Joseph Mcfiratht,Zhaoyuan Fang,Aidan Boyd,Ganghee Jang,Amir H. Mohammadi,Sandip Purnapatra,David Yambay,Sébastien Marcel,Mateusz Trokielewicz,Piotr Maciejewicz,Kevin W. Bowyer,Adam Czajka,Stephanie Schuckers,Juan Tapia,Sebastian Gonzalez,Meiling Fang,Naser Damer,Fadi Boutros,Arian Kuijper,Renu Sharma,Cunjian Chen,Arun Ross +22 more
TL;DR: LivDet-Iris 2020 as mentioned in this paper is an international competition series for iris PAD, which is open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection.
Proceedings ArticleDOI
Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network
TL;DR: Zhang et al. as discussed by the authors proposed an attention-based deep pixel-wise bi-nary supervision (A-PBS) method to detect iris presentation attack detection.
Journal ArticleDOI
Real Masks and Spoof Faces: On the Masked Face Presentation Attack Detection
Meiling Fang,Meiling Fang,Naser Damer,Naser Damer,Florian Kirchbuchner,Arjan Kuijper,Arjan Kuijper +6 more
TL;DR: In this article, the authors investigated the effect of masked attacks on face presentation attack detection (PAD) performance by using seven state-of-the-art PAD algorithms under different experimental settings.
Journal ArticleDOI
Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors
Naser Damer,C'esar Augusto Fontanillo L'opez,Meiling Fang,Noémie Spiller,Minh-Khoi Pham,Fadi Boutros +5 more
TL;DR: This work introduces the first synthetic-based MAD development dataset, namely the Synthetic Morphing Attack Detection Development dataset (SMDD), which is utilized successfully to train three MAD backbones where it proved to lead to high MAD performance, even on completely unknown attack types.