AUTOMATIC FACIAL EXPRESSION RECOGNITION USING FACIAL ANIMATION PARAMETERS AND MULTI-STREAM

《AUTOMATIC FACIAL EXPRESSION RECOGNITION USING FACIAL ANIMATION PARAMETERS AND MULTI-STREAM》由会员上传分享，免费在线阅读，更多相关内容在行业资料-天天文库。

1、AUTOMATICFACIALEXPRESSIONRECOGNITIONUSINGFACIALANIMATIONPARAMETERSANDMULTI-STREAMHMMSPetarS.AleksicandAggelosK.KatsaggelosDepartmentofElectricalandComputerEngineeringNorthwesternUniversity2145NorthSheridanRoad,Evanston,IL60208Email:{apetar,aggk}@ece.north

2、western.eduABSTRACTchangesoffacialfeatures.Sequencesoffacialimagesprovidesignificantinformationaboutthedynamicsoffacialexpressions.InthispaperwepresentanautomaticfacialexpressionFacialfeaturesusedforautomaticfacialexpressionrecognitionsystemthatutilizesmu

3、lti-streamHiddenMarkovanalysiscanbeobtainedusingtwoapproaches.Intheimage-Models(HMMs).TheproposedsystemusesFacialAnimationbasedapproach,thewholefaceimage,orimagesofpartsoftheParameters(FAPs),supportedbytheMPEG-4standard,asface,areprocessedinordertoobtainv

4、isualfeatures.Inthefeaturesdescribingfacialexpressions.Inparticular,theFAPsmodel-basedapproach,facemodelsareusedtodescribecontrollingthemovementoftheouter-lipsandeyebrowsaremovementofthevisualfeatures.Onlythemodelparametersthatusedasvisualfeaturesforclass

5、ification.Experimentswerechangeduringfacialexpressionsareusedforexpressionperformedunderseveraldifferentscenariosutilizingouter-liprecognition.PrincipalComponentAnalysis(PCA),LinearandeyebrowFAPsindividuallyandjointly.AnewapproachisDiscriminantAnalysis(LD

6、A),DiscreteCosineTransformproposedforintroducingfacialexpressionandFAPgroup(DCT),etc.,aremethodscommonlyusedtodecorrelatefacialdependentstreamweights.Theweightswerechosenbasedonfeaturesanddecreasetheirdimensionality.Suchfacialfeaturesthefacialexpressionre

7、cognitionresultsobtainedwhenFAPprovidemorereliabletrainingofclassificationsystemsandgroupstreamsareutilizedindividually.Theproposedmulti-improvetheirperformance.Inordertoperformperson-streamHMMfacialexpressionrecognitionsystemachievesindependentautomaticf

8、acialexpressionrecognitionitisrelativereductionoftheexpressionrecognitionerrorof44%,importanttonormalizethevaluesthatcorrespondtofacialcomparedtothesingle-streamHMMsystem.featurechangesusingthefacialfeaturesextracte