Face Recognition Systems Under Spoofing Attacks

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Detection and Continual Learning of Novel Face Presentation Attacks (2021, neutral): “Pavlidis Symosek [7] 2000 − Facial disguises 3DMAD [5] 2013 17 3D mask attacks I(2)BVSD [4] 2013 75 Facial disguises GUC-LiFFAD [8] 2015 80 2D print and replay MS-Spoof [3] 2015 21 2D print BRSU [11] 2016 50 3D masks EMSPAD [9] 2017 50 2D print MLFP [1] 2017 10 2D & 3D masks CASIA-SURF [12] 2020 100”
Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks (2020, background): “Nevertheless, there exists many studies showing that current face recognition algorithms are not robust to face presentation attacks [1], [2], [3], [4], [5].”
On the vulnerability of extended Multispectral face recognition systems towards presentation attacks (2017, background): “Recently, a vulnerability study on the multispectral face recognition with VIS and NIR spectrum is presented in [2].”
CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing (2019, background): “Another multi-modal dataset is Msspoof [19], containing visible and near-infrared images of real accesses and printed spooﬁng attacks with ≤ 21 objects.”