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dc.contributor.authorDas, Abhijit
dc.contributor.authorKunwar, Rituraj
dc.contributor.authorPal, Umapada
dc.contributor.authorFerrer Ballester, Miguel Angel
dc.contributor.authorBlumenstein, Michael
dc.contributor.editorRattani, A.
dc.contributor.editorRoli, F.
dc.contributor.editorGranger, E.
dc.date.accessioned2018-05-31T03:01:55Z
dc.date.available2018-05-31T03:01:55Z
dc.date.issued2015
dc.identifier.isbn9783319248653
dc.identifier.doi10.1007/978-3-319-24865-3_5
dc.identifier.urihttp://hdl.handle.net/10072/141702
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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer International Publishing
dc.publisher.placeSwitzerland
dc.relation.ispartofbooktitleAdaptive Biometric Systems: Recent Advances and Challenges
dc.relation.ispartofchapter5
dc.relation.ispartofpagefrom73
dc.relation.ispartofpageto96
dc.subject.fieldofresearchKnowledge Representation and Machine Learning
dc.subject.fieldofresearchcode170203
dc.titleAn Online Learning-Based Adaptive Biometric System
dc.typeBook chapter
dc.type.descriptionB1 - Chapters
dc.type.codeB - Book Chapters
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorKunwar, Rituraj
gro.griffith.authorDas, Abhijit


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