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Person authentication from neural activity of face-specific visual self-representation

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dc.contributor.authorYeom, Seul-Ki-
dc.contributor.authorSuk, Heung-Il-
dc.contributor.authorLee, Seong-Whan-
dc.date.accessioned2021-09-06T02:57:10Z-
dc.date.available2021-09-06T02:57:10Z-
dc.date.created2021-06-14-
dc.date.issued2013-04-
dc.identifier.issn0031-3203-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/103613-
dc.description.abstractIn this paper, we propose a new biometric system based on the neurophysiological features of face-specific visual self representation in a human brain, which can be measured by ElectroEncephaloGraphy (EEG). First, we devise a novel stimulus presentation paradigm, using self-face and non-self-face images as stimuli for a person authentication system that can validate a person's identity by comparing the observed trait with those stored in the database (one-to-one matching). Unlike previous methods that considered the brain activities of the resting state, motor imagery, or visual evoked potentials, there are evidences that the proposed paradigm generates unique subject-specific brain-wave patterns in response to self- and non-self-face images from psychology and neurophysiology studies. Second, we devise a method for adaptive selection of EEG channels and time intervals for each subject in a discriminative manner. This makes the system immune to forgery since the selected EEG channels and time intervals for a client may not be consistent with those of imposters in terms of the latency and amplitude of the brain-waves. Based on our experimental results and analysis, it is believed that the proposed person authentication system can be considered as a new biometric authentication system. (C) 2012 Elsevier Ltd. All rights reserved.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER SCI LTD-
dc.subjectPATTERN-RECOGNITION-
dc.subjectEEG-
dc.subjectBRAIN-
dc.titlePerson authentication from neural activity of face-specific visual self-representation-
dc.typeArticle-
dc.contributor.affiliatedAuthorSuk, Heung-Il-
dc.contributor.affiliatedAuthorLee, Seong-Whan-
dc.identifier.doi10.1016/j.patcog.2012.10.023-
dc.identifier.scopusid2-s2.0-84871305036-
dc.identifier.wosid000313858400006-
dc.identifier.bibliographicCitationPATTERN RECOGNITION, v.46, no.4, pp.1159 - 1169-
dc.relation.isPartOfPATTERN RECOGNITION-
dc.citation.titlePATTERN RECOGNITION-
dc.citation.volume46-
dc.citation.number4-
dc.citation.startPage1159-
dc.citation.endPage1169-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.subject.keywordPlusPATTERN-RECOGNITION-
dc.subject.keywordPlusEEG-
dc.subject.keywordPlusBRAIN-
dc.subject.keywordAuthorBiometrics-
dc.subject.keywordAuthorPerson authentication-
dc.subject.keywordAuthorElectroencephalography (EEG)-
dc.subject.keywordAuthorFace-specific visual self representation-
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