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基于CNN与关键区域特征的人脸表情识别算法.docx

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基于CNN与关键区域特征的人脸表情识别算法 Title:FacialExpressionRecognitionAlgorithmbasedonCNNandKeyRegionFeatures Abstract: Facialexpressionrecognitionhasgainedsignificantattentioninrecentyearsduetoitswiderangeofapplicationsinvariousfields,includinghuman-computerinteraction,emotionanalysis,andsocialrobotics.Inthispaper,weproposeanovelfacialexpressionrecognitionalgorithmthatcombinesConvolutionalNeuralNetworks(CNNs)withkeyregionfeatures.Thealgorithmaimstoimprovetheaccuracyandrobustnessoffacialexpressionrecognitionbyfocusingonthemostrelevantregionsoftheface. Introduction: Facialexpressionsplayacrucialroleinhumancommunication,conveyingemotions,intentions,andattitudes.Recognizingfacialexpressionsautomaticallyhasbecomeachallengingtaskinthefieldofcomputervision.Variousmethodshavebeenproposedtoaddressthisproblem,withCNNsbeingwidelyemployedfortheirexceptionalperformanceinimageclassificationtasks.However,accuratelyrecognizingfacialexpressionsstillremainsachallengeduetovariationsinpose,illumination,occlusion,andindividualdifferences. Methodology: Ourproposedalgorithmconsistsoftwomainsteps:featureextractionandexpressionclassification.Inthefeatureextractionstep,aCNNisusedtoextractdeepfeaturesfromtheinputfacialimage.ThepretrainedCNNmodel,suchasVGGNetorResNet,isfine-tunedusingalargefacialexpressiondataset.Thesedeepfeaturescapturethehigh-levelrepresentationsoffacialexpressions. Inthesecondstep,weincorporatetheconceptofkeyregionfeatures.Ratherthanconsideringthewholeface,wefocusonthemostinformativeregionsforexpressionrecognition.Varioustechniquescanbeemployedtoidentifythesekeyregions,suchasfaciallandmarkdetection,facialactionunitanalysis,orsaliencymaps.Thesekeyregionsarethenextractedandusedasinputstoaseparateclassificationmodel,suchasSupportVectorMachines(SVM)orRandomForests,forexpressionclassification. ExperimentalResults: Weevaluatetheperformanceofourproposedalgorithmonseveralpubliclyavailablefacialexpressiondatabases,suchasCK+,JAFFE,andFER2013.Theresultsdemonstratethatouralgorithmoutperformsexisting