SonarAuth: Using Around Device Sensing to Improve Smartwatch Behavioral Biometrics

Smartwatches are used by millions of people for applications in health, finance, and communication. As the computational power and range of applications supported by these devices expand, it is becoming more and more important to secure access to them. While various user authentication technologies have been extensively explored in smartphone use scenarios (e.g., FaceID, fingerprint, PIN, or pattern) the applicability of these approaches to smartwatches is typically limited due to the small watch form factor. To improve authentication on smartwatches, we propose SonarAuth, a novel user authentication system for unmodified commercial smartwatches using behavioral biometrics derived from motion, touch, and around-device motions. To capture in-air hand motions, we adapted an existing sonar system for smartwatches. We collected data from 24 participants from single touch to the watch screen with the thumb, index, and middle fingers. Using a multimodal deep learning classifier, we achieved a promising mean Equal Error Rate(EER) of 6.41% for user authentication based on a single thumb tap. We note that our system is usable and has good potential to be combined with other authentication modalities.