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基于卷积网络集成的面部表情识别方法.docx

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基于卷积网络集成的面部表情识别方法 Title:FacialExpressionRecognitionMethodbasedonConvolutionalNeuralNetworkEnsembles Abstract: Facialexpressionrecognitionplaysacrucialroleinvariousdomainssuchashuman-computerinteraction,affectivecomputing,andsocialrobotics.Withtheadvancementsindeeplearningandimageprocessingtechniques,convolutionalneuralnetwork(CNN)basedmodelshavedemonstratedremarkableperformanceinfacialexpressionrecognition.However,theperformanceofsingleCNNmodelscanbegreatlyaffectedbyvariationsindataset,illuminationconditions,andposeangles.Toovercometheselimitations,thispaperproposesafacialexpressionrecognitionmethodbasedontheensembleofconvolutionalneuralnetworks.Specifically,weapplyanensemblelearningtechniquetocombinethepredictionsofmultipleCNNmodels,therebyimprovingtheoverallaccuracyandrobustnessofthefacialexpressionrecognitionsystem.Experimentalresultsonbenchmarkdatasetsdemonstratetheeffectivenessandsuperiorityofourproposedapproachcomparedtostate-of-the-arttechniques. 1.Introduction Facialexpressionsarecrucialforhumancommunication,conveyingemotionsandintentions.Accuraterecognitionoffacialexpressionsenablesmachinestobetterunderstandandinteractwithhumanusers.Inrecentyears,deeplearningapproaches,particularlyCNNs,haverevolutionizedthefieldoffacialexpressionrecognitionduetotheirabilitytoautomaticallylearnrelevantfeaturesfromrawimagedata.However,singleCNNmodelsmaystrugglewithvariationsinlightingconditions,facialorientations,anddatasetbiases.Therefore,anensemblelearningapproachisproposedinthispapertoimprovetherobustnessandgeneralizationoffacialexpressionrecognitionsystems. 2.RelatedWork Thissectionprovidesanoverviewofexistingfacialexpressionrecognitiontechniques,includingtraditionalmethodsandrecentdeeplearningapproaches.IthighlightsthelimitationsofsingleCNNmodelsandpresentstherationaleforensemblelearning. 3.Methodology 3.1DatasetPreprocessing Thefirststepinourmethodinvolvespreprocessingthefacialexpressiondataset.Thisincludesfacedetection,facealignment,andnormalizationtechniquestoensureco