Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/74426
Campo DC Valoridioma
dc.contributor.authorDas, A.en_US
dc.contributor.authorKunwar, R.en_US
dc.contributor.authorPal, U.en_US
dc.contributor.authorFerrer, M. A.en_US
dc.contributor.authorBlumenstein, M.en_US
dc.date.accessioned2020-09-16T08:48:48Z-
dc.date.available2020-09-16T08:48:48Z-
dc.date.issued2015en_US
dc.identifier.issn2191-6586en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/74426-
dc.description.abstractIn the last decade, adaptive biometrics has become an emerging field of research. Considering the fact that limited work has been undertaken on adaptive biometrics using machine learning techniques, in this chapter we list and discuss a few out of many potential learning techniques that can be applied to build an adaptive biometric system. In order to illustrate the efficacy of one of the incremental learning techniques from the literature, we built an adaptive biometric system. For experimentation, we have used multi-modal ocular (sclera and iris) data. The preliminary results have been reported in the results section, which are very promising.en_US
dc.languageengen_US
dc.relation.ispartofAdaptive Biometric Systems: Recent Advances And Challengesen_US
dc.sourceAdaptive Biometric Systems: Recent Advances And Challenges[ISSN 2191-6586], p. 73-96, (2015)en_US
dc.subject3307 Tecnología electrónicaen_US
dc.subject.otherEnsembleen_US
dc.subject.otherClassifiersen_US
dc.subject.otherNetworken_US
dc.titleAn Online Learning-Based Adaptive Biometric Systemen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/978-3-319-24865-3_5en_US
dc.identifier.isi000371347500006-
dc.description.lastpage96en_US
dc.description.firstpage73en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid3164655-
dc.contributor.daisngid5777071-
dc.contributor.daisngid25227-
dc.contributor.daisngid233119-
dc.contributor.daisngid110880-
dc.description.numberofpages24en_US
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Das, A-
dc.contributor.wosstandardWOS:Kunwar, R-
dc.contributor.wosstandardWOS:Pal, U-
dc.contributor.wosstandardWOS:Ferrer, MA-
dc.contributor.wosstandardWOS:Blumenstein, M-
dc.date.coverdate2015en_US
dc.identifier.ulpgces
item.grantfulltextnone-
item.fulltextSin 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.orcid0000-0002-2924-1225-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameFerrer Ballester, Miguel Ángel-
Colección:Artículos
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