Monocular facial presentation-attack-detection:classifying near-infrared reflectance patterns

HIGHLIGHTS

• who: Ali Hassani et al. from the Information Systems, Security and Forensics Lab, University of Michigan-Dearborn, Dearborn, MI, USA have published the article: Monocular Facial Presentation-Attack-Detection:Classifying Near-Infrared Reflectance Patterns, in the Journal: (JOURNAL)
• what: This research proposes that near-infrared material spectroscopy can be used for robust facial presentation-attack-detection (PAD, also known as anti-spoofing). A mathematical surface-reflectance model is presented to show live faces should have predictably different reflectance distributions from their spoof counterparts. The aim of this paper is instead to propose a physics-informed . . .
  

   

  

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