  Remote photoplethysmography (rPPG) is a non-contact method for measuring
cardiac signals from facial videos, offering a convenient alternative to
contact photoplethysmography (cPPG) obtained from contact sensors. Recent
studies have shown that each individual possesses a unique cPPG signal
morphology that can be utilized as a biometric identifier, which has inspired
us to utilize the morphology of rPPG signals extracted from facial videos for
person authentication. Since the facial appearance and rPPG are mixed in the
facial videos, we first de-identify facial videos to remove facial appearance
while preserving the rPPG information, which protects facial privacy and
guarantees that only rPPG is used for authentication. The de-identified videos
are fed into an rPPG model to get the rPPG signal morphology for
authentication. In the first training stage, unsupervised rPPG training is
performed to get coarse rPPG signals. In the second training stage, an
rPPG-cPPG hybrid training is performed by incorporating external cPPG datasets
to achieve rPPG biometric authentication and enhance rPPG signal morphology.
Our approach needs only de-identified facial videos with subject IDs to train
rPPG authentication models. The experimental results demonstrate that rPPG
signal morphology hidden in facial videos can be used for biometric
authentication. The code is available at
https://github.com/zhaodongsun/rppg_biometrics.
