On the variability of TEOAE human identification and verification system
Jin Sung Kang · TSpace (University of Toronto) · 2018
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 show that our method has 7.56% performance increase for identification scenarios and 13.3% performance increase for verification scenarios over previous methods when averaged across all tests