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1、HindawiPublishingCorporationAdvancesinMechanicalEngineeringArticleID707106ResearchArticleDriverEyeStateClassificationBasedonCooccurrenceMatrixofOrientedGradientsBoZhang,WenjunWang,andBoChengStateKeyLaboratoryofAutomotiveSafetyandEnergy,TsinghuaUniversity,Beijing100084,ChinaCorre
2、spondenceshouldbeaddressedtoBoCheng;chengbo@tsinghua.edu.cnReceived19August2014;Accepted4November2014AcademicEditor:HongweiGuoCopyright©BoZhangetal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,whichpermitsunrestricteduse,distribution,andreproduct
3、ioninanymedium,providedtheoriginalworkisproperlycited.Accuratedetectionofdriver’seyestatebycomputervisioniscriticaltodriverdrowsinessmonitoring.Thehistogramoforientedgradients(HOG)iscommonlyusedasdescriptivefeatureofeyeimageforstateclassification.However,HOGoftensuffersfromtheli
4、mitoflocalgradientinformation.ThispaperproposesanewHOG-likefeatureofeyeimage,calledcooccurrencematrixoforientedgradients(CMOG),forthepurposeofmoreeffectivelyclassifyingtheeyestate.Byintroducingthecooccurrencematrix,theCMOGenhancestheabilityofdescribingglobalgradientinformationof
5、eyeimages.TheZJUeyeblinkdatabaseisusedasthebaselineimagesforperformancecomparison.TheclassificationresultsshowthattheaccuracyofCMOGreachesupto95.9%incomparisonwith91.9%byHOGunderthisdatabase.1.Introductionimportantfactorinthiskindofmethods.Manytypesoffeaturesfromeyeimageshavebee
6、nproposedbynowadaysThedrivereyestate,thatis,openingandclosing,istheresearchers,suchasHOG(HistogramsofOrientedGradi-mostsalientfacialexpressionrelatedtodriverdrowsiness.ents)[4],LBP(LocalBinaryPatterns)[5],GaborwaveletsEyestateclassificationbasedoncomputervisionplaysan[6],Eigeney
7、e[7],andASEF(AverageofSyntheticExactimportantroleinthefieldofdrowsinessmonitoring.ItisaFilters)[8].TakingLBP,forexample,itusesthedifferencechallengingtasktodetectdrowsinessfromeyeimagesowingbetweenpixelsinalocalscaletorepresenttheimagewhichtovariablefacialexpression,randomillumi
8、nation,andheadisinsensitivetoilluminationchangi
