SonicID: User Identification on Smart Glasses with Acoustic Sensing
Published in The Proceedings of the Association for Computing Machinery on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)/UbiComp, 2024
Recommended citation: Ke Li, Devansh Agarwal, Ruidong Zhang, Vipin Gunda, Tianjun Mo, Saif Mahmud, Boao Chen, François Guimbretière, and Cheng Zhang. 2024. SonicID: User Identification on Smart Glasses with Acoustic Sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 8, 4, Article 169 (December 2024), 28 pages. (Accepted Pending Minor Revisions) https://arxiv.org/abs/2406.08273
October 2025 (Expected)
Keyword: User Identification, Smart Glasses, Acoustic Sensing, Machine Learning
Smart glasses have become more prevalent as they provide an increasing number of applications for users. They store various types of private information or can access it via connections established with other devices. Therefore, there is a growing need for user identification on smart glasses. In this paper, we introduce a low-power and minimally-obtrusive system called SonicID, designed to authenticate users on glasses. SonicID extracts unique biometric information from users by scanning their faces with ultrasonic waves and utilizes this information to distinguish between different users, powered by a customized binary classifier with the ResNet-18 architecture. SonicID can authenticate users by scanning their face for 0.06 seconds. A user study involving 40 participants confirms that SonicID achieves a true positive rate of 97.4%, a false positive rate of 4.3%, and a balanced accuracy of 96.6% using just 1 minute of training data collected for each new user. This performance is relatively consistent across different remounting sessions and days. Given this promising performance, we further discuss the potential applications of SonicID and methods to improve its performance in the future.