On the vulnerability of extended Multispectral face recognition systems towards presentation 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”
Face Presentation Attack with Latex Masks in Multispectral Videos (2017, methodology): “Images vs Videos: Existing multispectral databases such as Multispectral Spoof Database (msspoof) [4] and Extended Multispectral Presentation Attack Database (EMSPAD) [19] consist of print attacks.”
CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing (2019, background): “EMSPAD [22] 2017 50 14,000∗ SpectraCamTM 7 bands 2 Print”
Multispectral Biometrics System Framework: Application to Presentation Attack Detection (2020, unknown): “The majority of existing PAD literature on multispectral data has relied on such commercial sensors for studying PAD paradigms (e.g., [15], [16], [17]).”
Presentation Attack Detection Methods for Face Recognition Systems (2017, background): “2015b; Libin 2014], one that combines time-frequency information with a texture descriptor [Raghavendra and Busch 2014a; Raghavendra et al. 2017], one that combines shape and texture [Määttä et al.”