Driver recognition using gaussian mixture models and decision fusion techniques

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dc.authorid0000-0001-6282-6703
dc.authorid0000-0003-0298-0690
dc.contributor.authorBenli, Kristin Surpuhien_US
dc.contributor.authorDüzağaç, Remzien_US
dc.contributor.authorEskil, Mustafa Taneren_US
dc.date.accessioned2015-07-14T23:48:11Z
dc.date.available2015-07-14T23:48:11Z
dc.date.issued2008
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractIn this paper we present our research in driver recognition. The goal of this study is to investigate the performance of different classifier fusion techniques in a driver recognition scenario. We are using solely driving behavior signals such as break and accelerator pedal pressure, engine RPM, vehicle speed; steering wheel angle for identifying the driver identities. We modeled each driver using Gaussian Mixture Models, obtained posterior probabilities of identities and combined these scores using different fixed mid trainable (adaptive) fusion methods. We observed error rates is low as 0.35% in recognition of 100 drivers using trainable combiners. We conclude that the fusion of multi-modal classifier results is very successful in biometric recognition of a person in a car setting.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationBenli, K. S., Düzağaç, R. & Eskil, M. T. (2008). Driver recognition using gaussian mixture models and decision fusion techniques. Lecture Notes in Computer Science, 5370, 803-811. doi:10.1007/978-3-540-92137-0_88en_US
dc.identifier.doi10.1007/978-3-540-92137-0_88
dc.identifier.endpage811
dc.identifier.isbn3540921362
dc.identifier.isbn9783540921363
dc.identifier.isbn9783540921370
dc.identifier.isbn3540921370
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.scopus2-s2.0-58549104184
dc.identifier.scopusqualityQ3
dc.identifier.startpage803
dc.identifier.urihttps://hdl.handle.net/11729/636
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-540-92137-0_88
dc.identifier.volume5370
dc.identifier.wosWOS:000264556900088
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakConference Proceedings Citation Index – Science (CPCI-S)en_US
dc.institutionauthorBenli, Kristin Surpuhien_US
dc.institutionauthorDüzağaç, Remzien_US
dc.institutionauthorEskil, Mustafa Taneren_US
dc.institutionauthorid0000-0001-6282-6703
dc.institutionauthorid0000-0003-0298-0690
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSpringer-Verlag Berlinen_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRecognitionen_US
dc.subjectVehicleen_US
dc.subjectGaussian Mixture Modelen_US
dc.subjectDecision Fusionen_US
dc.subjectBiometricsen_US
dc.subjectBlind source separationen_US
dc.subjectClassifiersen_US
dc.subjectCommunication channels (information theory)en_US
dc.subjectImage segmentationen_US
dc.subjectLearning systemsen_US
dc.subjectMixturesen_US
dc.subjectObject recognitionen_US
dc.subjectTrellis codesen_US
dc.subjectAccelerator pedalsen_US
dc.subjectClassifier fusionsen_US
dc.subjectDecision fusionen_US
dc.subjectDriving behaviorsen_US
dc.subjectError Rate (ER)en_US
dc.subjectFusion methodsen_US
dc.subjectGaussian mixture modelen_US
dc.subjectGaussian mixture models (GMMs)en_US
dc.subjectPosterior probabilitiesen_US
dc.subjectRecognitionen_US
dc.subjectVehicleen_US
dc.subjectVehicle speedsen_US
dc.subjectAutomobile driversen_US
dc.titleDriver recognition using gaussian mixture models and decision fusion techniquesen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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