G-PPG: A Gesture-related PPG-based Two-Factor Authentication for Wearable Devices

Verifying the user identity of wearable devices is crucial for system security, especially before sensitive operations like making financial payments. A PPG-based two-factor authentication can be a promising solution with widely deployed PPG (Photoplethysmography) sensors within wearable devices. Our observations find PPG readings reveal a significant relevance to the user’s hand motions, i.e., gestures, while the user’s heartbeat characteristics and wearing habits are also implicitly related, which can be utilized for user authentication. In this paper, we design G-PPG, a gesture-related PPG-based two-factor authentication mechanism that can non-intrusively validate the user’s identity. In G-PPG, gesture detection and segmentation and a specific feature set are proposed for accurate gesture-related PPG characteristic extraction. Moreover, an adaptive update scheme is proposed for the high accuracy of long-term authentication. Our experiments among 15 participants demonstrate that G-PPG can achieve a 90% accuracy in the long-term study.