Fisher linear discriminant based person identification using visual evoked potentials

A. Yazdani, A. Roodaki, S. H. Rezatofighi, K. Misaghian, S. K. Setarehdan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

34 Citations (Scopus)


Biometrics is the technique of uniquely recognizing a person among a group of people. It is usually performed based on one or more of human's intrinsic physical or behavioral traits. One such trait is the electroencephalogram (EEG) signal. In this paper, the feasibility of Visual Evoked Potential (VEP) in the gamma band of EEG signal, as a physiological trait, is studied, and used to identify individuals in a group of 20 people. To this end, the parameters of the AR model together with the peak of the power spectrum density (PSD) of the gamma band VEP signal (GMVEP) are considered as main useful features. Next, the Fisher's Linear Discriminant (FLD) is used to reduce the feature vector dimensions. Finally, the K Nearest Neighborhood (KNN) technique is employed to classify the data and the leave-one-out cross validation method is used for accuracy assessment. A correct classification rate of 100% is achieved.

Original languageEnglish
Title of host publication2008 9th International Conference on Signal Processing, ICSP 2008
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Print)9781424421794
Publication statusPublished - 8 Dec 2008
Externally publishedYes
EventInternational Conference on Signal Processing 2008 - Beijing, China
Duration: 26 Oct 200829 Oct 2008
Conference number: 9th


ConferenceInternational Conference on Signal Processing 2008
Abbreviated titleICSP 2008

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