User Authentication Based on the Integration of Musical Signals and Ear Canal Acoustics

This study presents a new biometric authentication system leveraging ear canal acoustic features for secure identity authentication. The proposed system can capture ear acoustics using an earphone integrated with a microphone, with musical signals as the probing signal. By taking the Ear Canal Transfer Function (ECTF) as the primary feature, we develop and implement a prototype that integrates data collection and deep feature extraction using particularly modified earphones. We then employ a convolutional neural network (CNN) to address the challenge of feature space overlap due to the diverse frequency components in musical signals. Our evaluation demonstrates the feasibility and the robustness of our method by using ear canal acoustics for user authentication, highlighting its potential for widespread application in security-sensitive environments.