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ECG Biometrics: A Template Selection Approach

dc.contributor.authorLourenço, André Ribeiro
dc.contributor.authorCarreiras, Carlos
dc.contributor.authorSilva, Hugo Plácido da
dc.contributor.authorFred, Ana Luísa Nobre
dc.date.accessioned2015-08-18T10:30:39Z
dc.date.available2015-08-18T10:30:39Z
dc.date.issued2014
dc.description.abstractElectrocardiography (ECG) biometrics is emerging as a viable biometric trait. Recent developments at the sensor level have shown the feasibility of performing signal acquisition at the fingers and hand palms, using one-lead sensor technology and dry electrodes. These new locations lead to ECG signals with lower signal to noise ratio and more prone to noise artifacts; the heart rate variability is another of the major challenges of this biometric trait. In this paper we propose a novel approach to ECG biometrics, with the purpose of reducing the computational complexity and increasing the robustness of the recognition process enabling the fusion of information across sessions. Our approach is based on clustering, grouping individual heartbeats based on their morphology. We study several methods to perform automatic template selection and account for variations observed in a person's biometric data. This approach allows the identification of different template groupings, taking into account the heart rate variability, and the removal of outliers due to noise artifacts. Experimental evaluation on real world data demonstrates the advantages of our approach.por
dc.identifier.issn978-1-4799-2921-4
dc.identifier.urihttp://hdl.handle.net/10400.21/4797
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherIEEE - Institute of Electrical and Electronics Engineers Inc.por
dc.relationSFRH/PROTEC/49512/2009
dc.relationMODELAÇÃO MULTI-MODAL DA COGNIÇÃO E EMOÇÃO
dc.relationLearning from Sequences (LearningS)
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6860081&openedRefinements%3D*%26pageNumber%3D8%26rowsPerPage%3D100%26queryText%3Dbiometrics
dc.subjectIdentificationpor
dc.subjectRecognitionpor
dc.subjectSystempor
dc.titleECG Biometrics: A Template Selection Approachpor
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleMODELAÇÃO MULTI-MODAL DA COGNIÇÃO E EMOÇÃO
oaire.awardTitleLearning from Sequences (LearningS)
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/FARH/SFRH%2FBD%2F65248%2F2009/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FEEI-SII%2F2312%2F2012/PT
oaire.citation.conferencePlaceNew Yorkpor
oaire.citation.endPage329por
oaire.citation.startPage324por
oaire.citation.titleIEEE International Symposium on Medical Measurements and Applications (MEMEA)por
oaire.fundingStreamFARH
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspor
rcaap.typeconferenceObjectpor
relation.isProjectOfPublication943715af-a8d5-4cc4-99f9-660ad2877407
relation.isProjectOfPublication48934697-f0f8-4b61-930b-e6ee339e6396
relation.isProjectOfPublication.latestForDiscovery943715af-a8d5-4cc4-99f9-660ad2877407

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