Continuous Authentication via Wrist Photoplethysmogram: An Extensive Study

Continuous authentication (CA) based on wrist photoplethysmogram (PPG) has been increasingly studied, but still requires further extensive investigation on PPG reliability over time and heart rates for real-world deployments. In this paper, we first analyze the inadequacy of current research, i.e., limited generalization capability for new users and insufficient experiments due to the absence of across-session data under different heart rates (HR). To address these problems, we then propose a unified and scalable feature extraction framework for wrist PPG-based CA. Given a continuous PPG waveform, our framework first encodes the PPG of each period separately, then extracts variability features contained in consecutive multi-period PPG for user authentication. On two datasets with a total of 155 subjects, we evaluate the performances of our system using different across-session levels and HR intervals, respectively. Despite more stringent experimental settings, we achieve even better performances than in previous studies. Using the subject-exclusive cross-validation protocol, our system reaches an average accuracy of 92.1% under the constraint of equal error rates in across-session evaluation, and average accuracy ranges from 86.4% (high HR) to 91.4% (low HR) for different HR intervals.