Face Presentation Attack with Latex Masks in Multispectral Videos

By Akshay Agarwal, Daksha Yadav, Naman Kohli, Richa Singh, Mayank Vatsa, Afzel Noore. 2017

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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”
Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks (2020, methodology): “For the MLFP dataset, we report only EER in the evaluation set since only two sets are available.”
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]).”
CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing (2019, unknown): “S/M-SWIR RGB/4 SWIR bands Print, 3D Mask BRSU [21] 2016 50+ VIS/AM-SWIR RGB/4 SWIR bands Print, 3D Mask EMSPAD [22] 2017 50 14,000 SpectraCamTM 7 bands 2 Print SMAD [23] 2017 130 VIS RGB 3D Mask MLFP [24] 2017 10 1,350 VIS/NIR/Thermal RGB/IR/Heat 2D/3D Mask Oulu-NPU [25] 2017 55 5,940 VIS RGB 2 Prin”