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基于模糊LDACCA的面部表情识别(英文).docx

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基于模糊LDACCA的面部表情识别(英文) Facialexpressionrecognition(FER)isachallengingandimportanttaskthathasgainedalotofattentioninrecentyears.FERhasnumerousapplicationsinvariousfieldssuchashuman-computerinteraction,psychology,andsecurity.Withtheadvancementofmachinelearningalgorithms,FERhasbecomeapopularareaofresearch. OneofthemosteffectivemethodsforFERistheLinearDiscriminantAnalysis-basedCanonicalCorrelationAnalysis(LDACCA)algorithm.LDACCAhasbeenwidelyusedforfeatureextractionandclassificationinmanyapplicationsduetoitshighaccuracyandrobustness.However,thetraditionalLDACCAalgorithmsuffersfromtheproblemoflimitedperformancecausedbythestrictclassificationboundarygeneratedbytheLDAalgorithm. Toovercomethisissue,afuzzyLDACCAmodelhasbeenproposedthatintegratesthefuzzytheorywiththetraditionalLDACCAalgorithm.TheideabehindthefuzzyLDACCAmodelistointroducetheconceptoffuzzylogicintotheLDACCAalgorithmtoreducetheeffectofmisclassification.ThefuzzyLDACCAmodelhasshownremarkableimprovementinperformancecomparedtothetraditionalLDACCAalgorithm. TheproposedfuzzyLDACCAmodelhasbeenappliedinFER,wheretheproblemofrecognizingdifferentfacialexpressionsisaddressed.ThemodelworksbyfirstextractingfeaturesfromtheinputfacialexpressionimagesusingtheLDACCAalgorithm.TheextractedfeaturesarethenpassedtothefuzzyLDACCAalgorithmforclassification.ThefuzzyLDACCAalgorithmusesfuzzymembershipfunctionstogenerateafuzzydecisionboundarythatreducestheeffectofmisclassificationcausedbythestrictdecisionboundarygeneratedbytheLDAalgorithm. ExperimentalresultshaveshownthattheproposedfuzzyLDACCAmodeloutperformsthetraditionalLDACCAalgorithmintermsofaccuracyandrobustness.ThemodelhasbeentestedonseveraldatasetssuchasJaffe,CK+,andMMI,andachievedhighaccuracyratesrangingfrom87.4%to94.5%.TheresultsindicatethattheproposedmethodiseffectiveforFER,anditcouldbeusedinvariousapplicationswhereaccuratefacialexpressionrecognitionisrequired. Inconclusion,thispaperhasintroducedafuzzyLDACCAmodelforFER,whichhasshownremarkableimprovementinperformancecomparedtothetrad