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This study presents a new technique for human recognition using transient auditory evoked potentials (AEPs). AEPs are electrical potentials that are triggered by stimulating ears with auditory stimulus reflecting the neural response from the cochlea to the auditory cortex. These signals feature some advantages over conventional biometric traits as they cannot be easily forged or stolen like fingerprints or faces. Moreover, these signals are cancellable and can be changed by modifying the auditory stimulus. This allows system reuse even if the registered signal was breached. To investigate the biometric potential of this signal, a database of ten subjects was collected where transient AEPs signals were recorded by stimulating the left and the right ears separately. Machine learning techniques were employed to extract unique features for each subject using 1D convolutional neural network. The proposed system was evaluated over single-session and two-session recordings. Moreover, a fusion of left and right ear stimulated AEP signals was adopted for performance improvement. Using single-session and twosession recordings, the proposed system achieved a correct recognition rate over 95% and an equal error rate below 7%. The achieved results show that AEPs carry subject discriminative features allowing the possibility of employing AEP signal as a biometric trait.

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