One-handed keystroke biometric identification competition

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dc.contributor.author Monaco, John V.
dc.contributor.author Perez, Gonzalo
dc.contributor.author Tappert, Charles C.
dc.contributor.author Bours, Patrick
dc.contributor.author Mondal, Soumik
dc.contributor.author Rajkumar, Sudalai
dc.contributor.author Morales Moreno, Aythami
dc.contributor.author Fiérrez, Julián
dc.contributor.author Ortega-García, Javier
dc.contributor.other UAM. Departamento de Tecnología Electrónica y de las Comunicaciones es_ES
dc.date.accessioned 2015-07-08T11:26:29Z
dc.date.available 2015-07-08T11:26:29Z
dc.date.issued 2015
dc.identifier.citation International Conference on Biometrics, ICB 2015. IEEE, 2015. 58-64 en_US
dc.identifier.isbn 978-1-4799-7824-3
dc.identifier.uri http://hdl.handle.net/10486/667300
dc.description Personal 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, 58-64 en_US
dc.description.abstract This 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.sponsorship The 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.extent 8 pág. es_ES
dc.format.mimetype application/pdf en
dc.language.iso eng en
dc.publisher Institute of Electrical and Electronics Engineers en_US
dc.rights © 2015 IEEE en_US
dc.title One-handed keystroke biometric identification competition en_US
dc.type conferenceObject en
dc.type bookPart en
dc.subject.eciencia Informática es_ES
dc.subject.eciencia Telecomunicaciones es_ES
dc.relation.publisherversion http://dx.doi.org/10.1109/ICB.2015.7139076
dc.identifier.doi 10.1109/ICB.2015.7139076
dc.identifier.publicationfirstpage 58
dc.identifier.publicationlastpage 64
dc.relation.eventdate May 19-22, 2015 en_US
dc.relation.eventplace Phuket (Thailand) en_US
dc.relation.eventtitle International Conference on Biometrics, ICB 2015 en_US
dc.type.version info:eu-repo/semantics/acceptedVersion en
dc.contributor.group Análisis y Tratamiento de Voz y Señales Biométricas (ING EPS-002) es_ES
dc.rights.accessRights openAccess en
dc.authorUAM Fierrez Aguilar, Julián (261834)
dc.authorUAM Morales Moreno, Aythami (264948)


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