Presentation Attack Detection for Face Recognition Using Light Field Camera

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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”
CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing (2019, unknown): “GUC-LiFFAD [15] 2015 80 4,826 LFC LFI 2 Print, Replay”
Attack Detection (2020, unknown): “Common attack methods include printed photographs, digital display attacks, video replays, and 3D mask fabrication (Raghavendra et al., 2015a).”
On the vulnerability of extended Multispectral face recognition systems towards presentation attacks (2017, background): “One of the earlier work has indicated the use of light-field cameras for detecting the presentation attacks in VIS spectrum [8].”
Presentation Attack Detection Methods for Face Recognition Systems (2017, methodology): “To illustrate the principle behind the PAD techniques developed in these sensor-based approaches, we consider the example of using a light field camera (LFC) as the face capture sensor [Raghavendra et al. 2015].”