TeethPass: Dental Occlusion-based User Authentication via In-ear Acoustic Sensing

Abstract

With the rapid development of mobile devices and the fast increase of sensitive data, secure and convenient mobile authentication technologies are desired. Except for traditional passwords, many mobile devices have biometric-based authentication methods (e.g., fingerprint, voiceprint, and face recognition), but they are vulnerable to spoofing attacks. To solve this problem, we study new biometric features which are based on the dental occlusion and find that the bone-conducted sound of dental occlusion collected in binaural canals contains unique features of individual bones and teeth. Motivated by this, we propose a novel authentication system, TeethPass, which uses earbuds to collect occlusal sounds in binaural canals to achieve authentication. We design an event detection method based on spectrum variance and double thresholds to detect bone-conducted sounds. Then, we analyze the time-frequency domain of the sounds to filter out motion noises and extract unique features of users from three aspects: bone structure, occlusal location, and occlusal sound. Finally, we design an incremental learning-based Siamese network to construct the classifier. Through extensive experiments including 22 participants, the performance of TeethPass in different environments is verified. TeethPass achieves an accuracy of 96.8% and resists nearly 99% of spoofing attacks.