Person identification using VEP signals and SVM classifiers

This paper is focused on proving the concept that Visually Evoked Potential (VEP) signals registered during experiments with mental task execution can be used for discrimination of individuals. The viability of the VEP-based person identification was successfully tested for a data base of 13 persons. Among various classifiers tested, Support Vector Machine (SVM) with Radial Basis Function (RBF) exhibits the best performance. Our study revealed that the duration of the VEPs required for a reliable identification is crucial for real time implementation of the proposed biometry paradigm. A post processing procedure is formulated in order to overcome the problem of static classification. Our ultimate goal is not to compete with the conventional biometry (such as fingerprint, iris or palm recognition systems) but to design a VEP -based biometry modality as a supplement (“a second opinion”) in clinical conditions. It could be used to verify a patient´s identity in medical records, prior to medical procedures or to detect early in advance abnormal mental states of the patient.