基于PCA和LDA融合的人脸鉴别方法研究

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1、华东交通大学硕士学位论文基于PCA和LDA融合的人脸鉴别方法研究姓名：张艳君申请学位级别：硕士专业：计算机应用技术指导教师：宋京伟20060609AbstractSTUDY0FFACEVERIFICATIONMETHODBASEDONFUSlONOFPCAANDLDAABSTRACTFacerecognitiontechnologymakesuseofcomputertoanalysisfaceimages．Itextractsusefulinformationfromfaceimagestodiscriminateidentity．On

2、etaskoffacerecognitiontechnologyisfaceverification．Faceverificationisaprocessofone-to-onematching，itsfunctionistoverifysomebody’Sidentityistrueornot．Inreality,thesecasesexistinmanyfields．Forexample，inadulteducation，whenstudentsgraduate，wehavetoverifytheiridentityusinghisp

3、hotographs．Thesephotographswerecollectedfromentranceexaminationandgraduation，andtheirtimeintervalisaboutfourtofiveyears．Consideringtheworkloadofmanualverifyingareverygreat，theverifyingmethodusingfaceimagesisanurgentdemand．SoitCanusecomputerreplacemanpowertospeedupverifica

4、tion．Inthispaper，itadoptsakindoffacenormalizationmethodwhichcombinesimage’SgreyintegralprojectioncurvesandHoughtransform．Aftertestingbyexperiments，thismethodhassomecharacteristics，suchasrapidspeed，highaccuracyandlessinfluencedbyexpression，deflexionandillumination．Ithas94．

5、9％accuracy．Atthesametime，faceimageshaveshifting，rotatinganddimensioninflexibilityafternormalization．Facenormalizationprovidesanadvantagedguaranteeforfollow-upfeatureextractionandverification．OnthebaseofanalysisandstudyaboutfacerecognitionmethodsbasedonPCAandLDA，weuseaface

6、verificationmethodbasedonthefusionofPCAandLDA．Inthismethod，itintroducesthreefusionmodes．ThereareMax，MaxMinandMeanmode．Thismethodcombinescorrelationanddistinctionofthetwoalgorithmspreferably．Itseriestheproblemoflackingtrainingsetswhichcombinesoriginalfaceimages，its1-orderp

7、rojection·combinedimagesandits2-orderprojection-combinedimages．Bytesting，theverificationresultsofthreefusionmodesinfusionmethodareprovedtooutperformtheindividualverificationalgorithmbasedonPCAorLDA．Itachieves6．39'o^-8．9％higheraccuracythanstandardPCAtechnologyand3．70／o--6．

8、3％higheraccuracythanstandardLDAtechnology．Moreover,itsfalserejectrateis1．05％'-'1．89％lowerthanlow