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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.
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C-Auth: Exploring the Feasibility of Using Egocentric View of Face Contour for User Authentication on Glasses
C-Auth is a novel authentication method for smart glasses that explores the feasibility of authenticating users using the facial contour lines from the nose and cheeks captured by a down-facing camera in the middle of the glasses. To evaluate the system, we conducted a user study with 20 participants in three sessions on different days. Our system correctly authenticates the target participant versus the other 19 participants (attackers) with a true positive rate of 98.0% (SD: 2.96%) and a false positive rate of 4.97% (2.88 %) across all three days. We conclude by discussing current limitations, challenges, and potential future applications for C-Auth.
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- Award ID(s):
- 2239569
- PAR ID:
- 10492773
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- The ACM International Symposium on Wearable Computing (ISWC'23)
- ISBN:
- 9798400701993
- Page Range / eLocation ID:
- 6 to 10
- Format(s):
- Medium: X
- Location:
- Cancun, Quintana Roo Mexico
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3594738.3611355
