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dc.contributor.authorZhao, C
dc.contributor.authorZhang, X
dc.contributor.authorZhang, B
dc.contributor.authorDang, Q
dc.contributor.authorLian, J
dc.date.accessioned2017-06-19T01:30:40Z
dc.date.available2017-06-19T01:30:40Z
dc.date.issued2013
dc.date.modified2014-06-23T03:57:06Z
dc.identifier.issn1751-956X
dc.identifier.doi10.1049/iet-its.2012.0005
dc.identifier.urihttp://hdl.handle.net/10072/60751
dc.description.abstractHuman-centric driver fatigue monitoring systems (DFMS) with integrated sensing, processing and networking aim to find solutions for traffic accidents and other relevant issues. A novel, efficient combined features extraction approach from Pyramid Histogram of Oriented Gradients (PHOG) and contourlet transform (CT) for fatigue expression descriptions of vehicle drivers is proposed, and a random subspace ensemble (RSE) of linear perception (LP) classifiers as the base classifier is then exploited for the classification of three predefined fatigue expressions classes, namely, awake expressions, moderate fatigue expressions and severe fatigue expressions. Holdout and cross-validation experiments are created, and the results show that combined features by RSE of LP classifiers outperform the other seven classifiers, that is, PHOG features by LP classifier, CT features by LP classifier and combined features by five individual LP classifiers. With combined features and RSE of LP classifiers, the average classification accuracies of three fatigue expression classes are over 92% in both the holdout and cross-validation experiments. Among the three fatigue expression classes, the class of severe fatigue expressions is the most difficult to recognise, and the classification accuracy is over 84% in both the holdout and cross-validation experiments, which shows the effectiveness of the proposed feature extraction method and RSE of LP classifiers in developing DFMS.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherThe Institution of Engineering and Technology
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom36
dc.relation.ispartofpageto45
dc.relation.ispartofissue1
dc.relation.ispartofjournalIET Intelligent Transport Systems
dc.relation.ispartofvolume7
dc.rights.retentionY
dc.subject.fieldofresearchElectrical and Electronic Engineering not elsewhere classified
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchcode090699
dc.subject.fieldofresearchcode0906
dc.titleDriver's fatigue expressions recognition by combined features from pyramid histogram of oriented gradient and contourlet transform with random subspace ensembles
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorZhang, Paul


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