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Conference Paper: Feature selection and channel optimization for biometric identification based on visual evoked potentials

TitleFeature selection and channel optimization for biometric identification based on visual evoked potentials
Authors
Issue Date2014
PublisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001228
Citation
The 19th International Conference on Digital Signal Processing (DSP), Hong Kong, China, 20-23 August 2014. In Proceedings of the International Conference on Digital Signal Processing, 2014, p. 772-776 How to Cite?
AbstractIn recent years, biometric identification has received general concerns around the world, and become a frontal and hot topic in the information age. Among the internal biometric traits, electroencephalogram (EEG) signals have emerged as a prominent characteristic due to the high security, uniqueness and impossibility to steal or mimic. In this paper, individual difference of visual evoked potentials (VEPs) with cognition task were investigated, in addition, a feature selection and channel optimization strategy was developed for the VEPs based biometric identification system, where three different methods, including genetic algorithm (GA), Fisher discriminant ratio (FDR), and recursive feature elimination (RFE) were employed. In our experiments with 20 healthy subjects, the classification accuracy by support vector machine (SVM) reached up to 97.25% with AR model parameters, compared to 96.25% before optimization, and 32 channels of most discriminative were eventually selected from 64 channels with best performance. Results in this study revealed the feasibility of VEPs based EEG to be used for biometric identification. The proposed optimization algorithm was shown to have the ability to effectively improve the identification accuracy as well as simplifying the system. Further investigate may provide a novel idea for the individual difference analysis of EEG and for its practical design and optimization in the field of biometrics in the future.
Persistent Identifierhttp://hdl.handle.net/10722/204089

 

DC FieldValueLanguage
dc.contributor.authorBai, Yen_US
dc.contributor.authorZhang, Zen_US
dc.contributor.authorMing, Den_US
dc.date.accessioned2014-09-19T20:05:05Z-
dc.date.available2014-09-19T20:05:05Z-
dc.date.issued2014-
dc.identifier.citationThe 19th International Conference on Digital Signal Processing (DSP), Hong Kong, China, 20-23 August 2014. In Proceedings of the International Conference on Digital Signal Processing, 2014, p. 772-776en_US
dc.identifier.urihttp://hdl.handle.net/10722/204089-
dc.description.abstractIn recent years, biometric identification has received general concerns around the world, and become a frontal and hot topic in the information age. Among the internal biometric traits, electroencephalogram (EEG) signals have emerged as a prominent characteristic due to the high security, uniqueness and impossibility to steal or mimic. In this paper, individual difference of visual evoked potentials (VEPs) with cognition task were investigated, in addition, a feature selection and channel optimization strategy was developed for the VEPs based biometric identification system, where three different methods, including genetic algorithm (GA), Fisher discriminant ratio (FDR), and recursive feature elimination (RFE) were employed. In our experiments with 20 healthy subjects, the classification accuracy by support vector machine (SVM) reached up to 97.25% with AR model parameters, compared to 96.25% before optimization, and 32 channels of most discriminative were eventually selected from 64 channels with best performance. Results in this study revealed the feasibility of VEPs based EEG to be used for biometric identification. The proposed optimization algorithm was shown to have the ability to effectively improve the identification accuracy as well as simplifying the system. Further investigate may provide a novel idea for the individual difference analysis of EEG and for its practical design and optimization in the field of biometrics in the future.-
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001228-
dc.relation.ispartofProceedings of the International Conference on Digital Signal Processingen_US
dc.rightsProceedings of the International Conference on Digital Signal Processing. Copyright © I E E E.-
dc.rights© 2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleFeature selection and channel optimization for biometric identification based on visual evoked potentialsen_US
dc.typeConference_Paperen_US
dc.identifier.emailBai, Y: tsdwx56@hku.hken_US
dc.identifier.emailZhang, Z: zgzhang@eee.hku.hken_US
dc.identifier.authorityZhang, Z=rp01565en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDSP.2014.6900769-
dc.identifier.hkuros238877en_US
dc.identifier.hkuros241194-
dc.identifier.spage772-
dc.identifier.epage776-
dc.publisher.placeUnited State-

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