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.

Biometrics are widely used for user identification/authentication, but the fact has rarely been noticed that general capacitive touchscreens can reveal user identities by touch signals. This paper proposes a new biometric method with inherent liveness detection for reliable user recognition based on the cardiac signal captured by the capacitive touchscreen, namely Capacitive Plethysmogram (CPG). And a systematic framework is designed for CPG collection, processing, and exploitation to identify users. Specifically, since the finger usually forms capacitors with multiple sensing electrodes during touching, we can extract several CPG signals simultaneously from the screen output. Then we propose a series of preprocessing algorithms to filter CPG for signal quality enhancement. Finally, to further leverage filtered CPG signals and extract efficient features for identifying users, we build an encoder based on 3D attention CNN and metric learning. Experimental results demonstrate that the proposed method can achieve an average accuracy of 96.73%, FAR of 3.03%, and FRR of 7.35% in the laboratory environment, which reveals the potential of CPG for user privacy protection and data security on various devices laced with capacitive touchscreens.