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One-handed keystroke biometric identification competition

dc.contributor.authorMonaco, John V.
dc.contributor.authorPerez, Gonzalo
dc.contributor.authorTappert, Charles C.
dc.contributor.authorBours, Patrick
dc.contributor.authorMondal, Soumik
dc.contributor.authorRajkumar, Sudalai
dc.contributor.authorMorales Moreno, Aythami 
dc.contributor.authorFiérrez Aguilar, Julián 
dc.contributor.authorOrtega García, Javier 
dc.contributor.otherUAM. Departamento de Tecnología Electrónica y de las Comunicacioneses_ES
dc.date.accessioned2015-07-08T11:26:29Z
dc.date.available2015-07-08T11:26:29Z
dc.date.issued2015
dc.identifier.citationInternational Conference on Biometrics, ICB 2015. IEEE, 2015. 58-64en_US
dc.identifier.isbn978-1-4799-7824-3
dc.identifier.urihttp://hdl.handle.net/10486/667300
dc.descriptionPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. V. Monaco, G. Perez, C. C. Tappert, P. Bours, S. Modal, S. Rajkumar, A. Morales, J. Fierrez, and J. Ortega-Garcia, "One-handed Keystroke Biometric Identification Competition", in International Conference on Biometrics, ICB 2015, 58-64en_US
dc.description.abstractThis work presents the results of the One-handed Keystroke Biometric Identification Competition (OhKBIC), an official competition of the 8th IAPR International Conference on Biometrics (ICB). A unique keystroke biometric dataset was collected that includes freely-typed long-text samples from 64 subjects. Samples were collected to simulate normal typing behavior and the severe handicap of only being able to type with one hand. Competition participants designed classification models trained on the normally-typed samples in an attempt to classify an unlabeled dataset that consists of normally-typed and one-handed samples. Participants competed against each other to obtain the highest classification accuracies and submitted classification results through an online system similar to Kaggle. The classification results and top performing strategies are described.en_US
dc.description.sponsorshipThe authors would like to acknowledge the support from the National Science Foundation under Grant No. 1241585. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the US government.en_US
dc.format.extent8 pág.es_ES
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2015 IEEEen_US
dc.titleOne-handed keystroke biometric identification competitionen_US
dc.typeconferenceObjecten
dc.typebookParten
dc.subject.ecienciaInformáticaes_ES
dc.subject.ecienciaTelecomunicacioneses_ES
dc.relation.publisherversionhttp://dx.doi.org/10.1109/ICB.2015.7139076
dc.identifier.doi10.1109/ICB.2015.7139076
dc.identifier.publicationfirstpage58
dc.identifier.publicationlastpage64
dc.relation.eventdateMay 19-22, 2015en_US
dc.relation.eventplacePhuket (Thailand)en_US
dc.relation.eventtitleInternational Conference on Biometrics, ICB 2015en_US
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen
dc.contributor.groupAnálisis y Tratamiento de Voz y Señales Biométricas (ING EPS-002)es_ES
dc.rights.accessRightsopenAccessen
dc.authorUAMFierrez Aguilar, Julián (261834)
dc.authorUAMMorales Moreno, Aythami (264948)
dc.facultadUAMEscuela Politécnica Superior


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