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.

翻译：远程光电容积脉搏波（rPPG）是一种通过面部视频测量心脏信号的非接触式方法，为接触式传感器获取的接触式光电容积脉搏波（cPPG）提供了便捷的替代方案。近期研究表明，每个人都拥有独特的cPPG信号形态，可作为生物特征标识符，这启发我们利用从面部视频中提取的rPPG信号形态进行人员身份认证。由于面部外观与rPPG在面部视频中相互混合，我们首先对面部视频进行去身份化处理以移除面部外观，同时保留rPPG信息，这既保护了面部隐私，也确保仅使用rPPG进行认证。去身份化后的视频被输入rPPG模型以获取用于身份认证的rPPG信号形态。在第一训练阶段，通过无监督rPPG训练获取粗粒度rPPG信号。在第二训练阶段，通过引入外部cPPG数据集进行rPPG-cPPG混合训练，以实现rPPG生物特征认证并增强rPPG信号形态。我们的方法仅需带有受试者ID的去身份化面部视频即可训练rPPG认证模型。实验结果表明，隐藏在面部视频中的rPPG信号形态可用于生物特征认证。代码发布于 https://github.com/zhaodongsun/rppg_biometrics。