A Novel Earprint: Stimulus-Frequency Otoacoustic Emission for Biometric Recognition

Otoacoustic emission (OAE) biometrics are inherently robust to replay and falsification attacks. The widely studied transient-evoked OAE (TEOAE) is non-stationary and offers biometric value only in normal-hearing individuals since it is more susceptible to hearing loss. To address these issues, this paper presents a novel yet promising OAE biometric modality-stimulus-frequency OAE (SFOAE). Unlike TEOAE, SFOAE is a highly stationary signal whose fine structures are idiosyncratic to an individual, and relatively stable over time, making it easier to be a biometric without additional complex feature extraction. Moreover, SFOAE is even present in ears with 50 dB HL hearing loss, applicable to hearing-impaired users. In this paper, SFOAE spectra in response to three stimulus levels are fused in the feature level to consolidate different information, followed by a linear discriminant analysis or a multi-kernel convolutional neural network to further reduce the intra-subject variability and increase the inter-subject variability. Tested on a large cohort of subjects containing varying levels of deafness, the SFOAE-based biometric system yields an equal error rate of 0.541% and 1.364% in closed-set and open-set verification scenarios, respectively. In an identification mode, 99.43% and 97.37% accuracies are attained for closed-set and open-set protocols, respectively. In particular, we observe perfect performance in a population restricted to normal hearing in closed-set scenarios. The reason why the system performs well has been examined based on several comparative tests. Although there are implementation issues to be resolved before SFOAE can be applied in the field of biometric, this paper preliminarily demonstrates the basis and excellent potential of SFOAE as a biometric.