EarPass: Continuous User Authentication with In-ear PPG
In the rapidly expanding universe of smart IoT, earable devices, such as smart headphones and hearing aids, are gaining remarkable popularity. As we anticipate a future where a myriad of sophisticated applications—interaction, communication, health monitoring, and fitness guidance—migrate to earable devices handling sensitive and private information, the need for a robust, continuous authentication system for these devices becomes more critical than ever. Yet, current earable-based solutions, which rely predominantly on audio signals, are marred by inherent drawbacks such as privacy concerns, high costs, and noise interference. In light of these challenges, we investigate the potential of leveraging photoplethysmogram (PPG) sensors, which monitor key cardiac activities and reflect the uniqueness of an individual’s cardiac system, for earable authentication. Our study presents EarPass, an innovative ear-worn system that introduces a novel pipeline for the extraction and classification of in-ear PPG features to enable continuous user authentication. Initially, we preprocess the input in-ear PPG signals to facilitate this feature extraction and classification. Additionally, we present a method for detecting and eliminating motion artifacts (MAs) caused by head motions. Through extensive experiments, we not only demonstrate the effectiveness of our proposed design, but also establish the feasibility of using in-ear PPG for continuous user authentication—a significant stride towards more secure and efficient earable technologies.