Abstract
Camera-based remote photoplethysmography (rPPG) technology has shown a
promising future in contact-free cardiac and other smart health
applications. The rPPG technology typically requires facial videos as a
source input, which may lead to identity-privacy concerns. Facial videos
are sensitive and contain subjects’ identifiable appearance features.
Coupled with the health information potentially revealed by rPPG
techniques, the compounding sensitivity has been a major obstacle to
encouraging the sharing of facial rPPG video datasets in the research
community to foster the advancement of the field. This paper
investigates a suite of anonymization transforms that remove the
identifiable appearance features in facial videos and retain the
physiological signals for rPPG analysis. After the transformation, the
facial videos are de-identified and may be shared in public with little
risk of identity-privacy leakage. The proposed algorithm offers tunable
options to balance the physiological fidelity and the
identity-protecting strength to meet different levels of privacy
requirements. A human subject study has been carried out to understand
– both qualitatively and quantitatively – the perceived strength and
efficacy of privacy protection by these anonymization techniques in
de-identifying the facial videos and maintaining the physiological
signals.