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1、ABSTRACTMicro-expressionisabriefandinvoluntaryfacialexpressionwhichonlycanbedetectedbyafewofpeopleinreallife.Comparedwithnormalfacialexpression,micro-expressionismorelikelytorevealpeople’sfeelingandmotivation.Itusuallyappearswhenpeopletrytoconcealorrepresstheirrealemotions,somicro-exp
2、ressionisaneffectiveclueforlieindication.Asforitsshortdurationandlowintensity,theresearchofautomaticmicro-expressiondetectionandrecognitionisachallengingtask.Thispaperpresentsamethodformicro-expressionfeatureextractionbaseonmonogeniclocalbinarypattern,andappliesittodetectmicro-express
3、ioninstaticimagesanddynamicimagesequences.Themainworkisasfollows:(1)Faciallandmarklocation.Asthecomputationinparameterizedappearancemodels(e.g.,ActiveAppearanceModel)isexpensiveandcomplexity,thispaperpresentsthesuperviseddescentmethod(SDM).SDMextractfeaturesbylearningthedescentdirecti
4、onsofthesequencesinasupervisedmanner,whichreducesthecalculation..(2)Thispaperpresentsthemonogenicbinarypattern(MBP)formicro-expressionfeatureextraction.Thismethodusesfewerconvolutionstoextractmorecompactfeaturevectors,whichreducestimeandspacecomplexity.Dynamicfeaturecanrepresentmoreco
5、mprehensiveinformationofmicro-expression,soMBPisexpandedtothreeorthogonalplanesMBPtoclassifybetter.(3)Asprobabilitystatisticsmodels,HiddenMarkovModelshasbeenappliedinspeechrecognitionandfacialexpressionrecognitionsuccessfully.AmethodbasedontheHiddenMarkovModelsispresentedherewhichgain
6、saHMMforeachmicro-expression.TheexperimentssimulatebyMATLAB.Therearetwomicro-expressionimagedatabasesusedintheexperiments,CASMEandSMIC.ExperimentalresultsshowthattheproposedmethodhasbetterperformancethanGaborandreducethecomplexityintimeandspace.KEYWORDS:micro-expression,superviseddesc
7、entmethod,monogenicsignal,MBP,HiddenMarkovModelsII目錄第一章緒論......................................................................................................................-1-1.1課題研究背景與意義...............................................................................................
8、..-1-
