PAD-Phys: Exploiting Physiology for Presentation Attack Detection in
  Face Biometrics
Luis F. Gomez Julian Fierrez Aythami Morales Mahdi Ghafourian Ruben Tolosana Imanol Solano Alejandro Garcia Francisco Zamora-Martinez
Presentation Attack Detection (PAD) is a crucial stage in facial recognition
systems to avoid leakage of personal information or spoofing of identity to
entities. Recently, pulse detection based on remote photoplethysmography (rPPG)
has been shown to be effective in face presentation attack detection.
  This work presents three different approaches to the presentation attack
detection based on rPPG: (i) The physiological domain, a domain using
rPPG-based models, (ii) the Deepfakes domain, a domain where models were
retrained from the physiological domain to specific Deepfakes detection tasks;
and (iii) a new Presentation Attack domain was trained by applying transfer
learning from the two previous domains to improve the capability to
differentiate between bona-fides and attacks.
  The results show the efficiency of the rPPG-based models for presentation
attack detection, evidencing a 21.70% decrease in average classification error
rate (ACER) (from 41.03% to 19.32%) when the presentation attack domain is
compared to the physiological and Deepfakes domains. Our experiments highlight
the efficiency of transfer learning in rPPG-based models and perform well in
presentation attack detection in instruments that do not allow copying of this
physiological feature.