Neural Network Architecture and Transient Evoked Otoacoustic Emission (TEOAE) Biometrics for Identification and Verification
Jin Sung Kang, Yuri Lawryshyn, Dimitrios Hatzinakos · IEEE Transactions on Information Forensics and Security · 2019
This study presents a deep neural network architecture that achieves state of the art multi-session verification and identification performance for Transient Evoked Otoacoustic Emission (TEOAE) biometric system. TEOAE is a 20ms long response generated by the ear that is naturally strong against falsification, and replay attacks. It can be measured using a device with a speaker and multiple microphones. Previous TEAOE authentication methods focused on single-session or mixed-session performance. Our method focuses on multi-session authentication performance. We train a neural network model that generates a TEOAE embedding that is separable in Euclidean space by using the triplet loss function. These embeddings are used to create identity templates which are used to authenticate the user. We achieved identification accuracy of 99.3 ± 1.04%, and achieved an EER(Equal Error Rate) of 0.187 ± 0.146% for verification scenarios. Our method has achieved 7.56% performance increase for identification scenarios and 13.3% performance increase for verification scenarios over previous methods when averaged across all tests.