Secure User Verification and Continuous Authentication via Earphone IMU

Biometric plays an important role in user authentication. However, the most widely used biometrics, such as facial feature and fingerprint, are easy to capture or record, and thus vulnerable to spoofing attacks. On the contrary, intracorporal biometrics, such as electrocardiography and electroencephalography, are hard to collect, and hence more secure for authentication. Unfortunately, adopting them is not user-friendly due to their complicated collection methods or inconvenient constraints on users. In this paper, we propose a novel biometric-based authentication system, namely
MandiPass
.
MandiPass
leverages inertial measurement units, which have been widely deployed in portable devices, to collect intracorporal biometric from the vibration of user's mandible. It provides not only one-time verification function but also continuous authentication function. Both the two functions are secure and user-friendly. We theoretically validate the feasibility of
MandiPass
and develop a series of deep learning techniques for effective biometric extraction. We also utilize a Gaussian matrix to defend against replay attacks. Extensive experiment results with 34 volunteers show that
MandiPass
can achieve low equal error rate, even under various harsh environments.