Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/106072
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dc.contributor.authorFerrer Ballester, Miguel Ángelen_US
dc.contributor.authorDiaz Cabrera, Moisesen_US
dc.contributor.authorCarmona Duarte, María Cristinaen_US
dc.contributor.authorRejean Plamondonen_US
dc.date.accessioned2021-03-22T14:48:23Z-
dc.date.available2021-03-22T14:48:23Z-
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/10553/106072-
dc.description.abstractOne of the biggest challenges in on-line signature verification is the detection of counterfeited signatures. Recently, novel schemes based on the kinematic theory of rapid human movements and its associated Sigma-Lognormal model has been proposed to improve the detection of on-line skilled forgeries. But for a more realistic and reliable estimation of the forgery detection rate, we would need more challenging on-line forgeries than those included in current databases. To get better on-line skilled forgeries, this paper aimed at leveraging the Sigma-Lognormal model to improve the skill of any online forged signature. Specifically, we propose to replace the original velocity profile of any on-line signature by a synthetic Sigma-Lognormal profile. The new profile emulates a genuine-like velocity profiles without modifying the original ballistic trajectory. Experimental results were performed with the 132 on-line users of publicly BiosecureID database. It is shown that the detection rate of forgeries is significantly worsened when the velocity profile is replaced by the synthetic one. A countermeasure to detect this kind of improved fake signatures is also proposed.en_US
dc.languageengen_US
dc.sourceICPRAI 2018. International Conference on Pattern Recognition and Artificial Intelligence. Montreal, Canadáen_US
dc.subject1203 Ciencia de los ordenadoresen_US
dc.subject.otherAutomatic Signature Verificationen_US
dc.subject.otherSigma-Lognormal modelen_US
dc.subject.otherforged signaturesen_US
dc.titleImproving on-line signature skillfulnessen_US
dc.typeinfo:eu-repo/semantics/conferenceobjecten_US
dc.typeConferenceObjecten_US
dc.relation.conferenceInternational Conference on Pattern Recognition and Artificial Intelligenceen_US
dc.description.lastpage799en_US
dc.description.firstpage795en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.utils.revisionen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-TELen_US
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Física-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0002-2924-1225-
crisitem.author.orcid0000-0003-3878-3867-
crisitem.author.orcid0000-0002-4441-6652-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameFerrer Ballester, Miguel Ángel-
crisitem.author.fullNameDíaz Cabrera, Moisés-
crisitem.author.fullNameCarmona Duarte, María Cristina-
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