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Learning Bayesian Network Classifiers for Facial Expression Recognition

Learning Bayesian Network Classifiers for Facial Expression Recognition

ID：40363735

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页数：7页

时间：2019-08-01

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1、LearningBayesianNetworkClassifiersforFacialExpressionRecognitionusingbothLabeledandUnlabeledDataIraCohen,NicuSebe,FabioG.Cozman,MarceloC.Cirelo,ThomasS.HuangBeckmanInstitute,UniversityofIllinoisatUrbana-Champaign,IL,USA,iracohen,huang@ifp.uiuc.eduLeidenInstituteofAdvancedComputerS

2、cience,LeidenUniversity,TheNetherlands,nicu@liacs.nlEscolaPolitecnica,´UniversidadedeSao˜Paulo,Sao˜Paulo,Brazilfgcozman,marcelo.cirelo@usp.brAbstracttailedreviewofmanyoftheresearchdoneinrecentyears).AllthesemethodsaresimilarinthattheyfirstextractsomeUnderstandinghumanemotionsisoneofthen

3、ecessaryfeaturesfromtheimagesorvideo,thenthesefeaturesareskillsforthecomputertointeractintelligentlywithhumanusedasinputsintoaclassificationsystem,andtheoutcomeusers.Themostexpressivewayhumansdisplayemotionsisoneofthepreselectedemotioncategories.Theydifferisthroughfacialexpressions.Inthisp

4、aper,wereportonmainlyinthefeaturesextractedandintheclassifiersusedtoseveraladvanceswehavemadeinbuildingasystemfordistinguishbetweenthedifferentemotions.classificationoffacialexpressionsfromcontinuousvideoWehavedevelopedarealtimefacialexpressionrecogni-input.WeuseBayesiannetworkclassifiersfor

5、classifyingtionsystem.Thesystemusesamodelbasednon-rigidfaceexpressionsfromvideo.Oneofthemotivatingfactorinus-trackingalgorithmtoextractmotionfeaturesthatserveasingtheBayesiannetworkclassifiersistheirabilitytohan-inputtoaBayesiannetworkclassifierusedforrecognizingdlemissingdata,bothduringinf

6、erenceandtraining.Infacialexpressions[5].Inoursystem,aswithallotherpastparticular,weareinterestedintheproblemoflearningwithresearchinfacialexpressionrecognition,learningtheclassi-bothlabeledandunlabeleddata.Weshowthatwhenus-fierswasdoneusinglabeleddataandsupervisedlearningal-ingunlabeledda

7、tatolearnclassifiers,usingcorrectmodel-gorithms.Oneofthechallengesfacingresearchersattempt-ingassumptionsiscriticalforachievingimprovedclassifi-ingtodesignfacialexpressionrecognitionsystemsisthecationperformance.Motivatedbythis,weintroduceaclas-relativelysmallamountof

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