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用于人脸识别的改进MKD-SRC方法.docx

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用于人脸识别的改进MKD-SRC方法 Title:AnImprovedMKD-SRCMethodforFaceRecognition Abstract: Inrecentyears,facerecognitiontechnologyhasgainedsignificantattentionduetoitswiderangeofapplications,suchassecuritysystems,surveillance,andbiometricidentification.TheMulti-kernelDiscriminantSparseRepresentationClassification(MKD-SRC)methodhasemergedasapromisingtechniqueforfacerecognition.ThispaperpresentsanimprovedMKD-SRCmethodthatenhancestheaccuracyandefficiencyoffacerecognitionsystems.Theproposedmethodcombinesmultiplekernelsanddiscriminantfeaturesinasparserepresentationframework,enablingimprovedrecognitionrates. 1.Introduction: Facerecognitionisachallengingtaskduetovariationsinlightingconditions,pose,occlusions,andfacialexpressions.TheMKD-SRCmethodhasbeensuccessfulinfacerecognitionduetoitsabilitytoselectdiscriminativefeaturesandeffectivelyrepresentfaceimages.However,theoriginalMKD-SRCmethodhaslimitationsintermsofaccuracyandcomputationalefficiency.ThispaperproposesanimprovedMKD-SRCmethodtoovercometheselimitations. 2.LiteratureReview: Inthissection,acomprehensivereviewoftherelevantliteratureonfacerecognitionandcurrenttechniquesispresented.Variousmethods,suchasPrincipalComponentAnalysis(PCA),LinearDiscriminantAnalysis(LDA),andSparseRepresentationClassification(SRC),arediscussed.Thelimitationsofthesemethodsarehighlighted,leadingtothemotivationforthisresearch. 3.ProposedMethodology: TheproposedimprovedMKD-SRCmethodconsistsofthefollowingsteps: 3.1.Preprocessing:Theinputfaceimagesarepreprocessedtoenhancethequalityandreducenoise.Techniquessuchasfacealignment,normalization,andilluminationnormalizationareapplied. 3.2.FeatureExtraction:DiscriminantfeaturesareextractedusingtheDiscriminantAnalysis(DA)method.Thisextractionaimstomaximizethebetween-classscatterandminimizethewithin-classscatter. 3.3.SparseRepresentation:Theextractedfeaturesareusedtorepresentthefaceimagessparsely.Thisisachievedbysolvingasparserepresentationproblem,wherethecoefficientsarelearnedfromatrainingset. 3.4.Kernelization:Multipleker