Fisher Linear Discriminant Based Person Identification Using Visual Evoked Potentials
Biometrics is the technique of uniquely recognizing a person among a group of people.It is usually performed based on one or more of humans 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 Fishers 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.
A.Yazdani A.Roodaki S.H.Rezatofighi K.Misaghian S.K.Setarehdan
Control and Intelligent Processing Centre of Excellence,Faculty of ECE,University of Tehran,Tehran,Iran
国际会议
9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)
北京
英文
2008-10-26（万方平台首次上网日期，不代表论文的发表时间）