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改进的SIFT特征人脸识别方法ADSIFT.docx

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改进的SIFT特征人脸识别方法ADSIFT ADSIFT:AnImprovedSIFT-basedFaceRecognitionMethod Abstract: Facerecognitionisachallengingtaskincomputervision,withnumerousreal-worldapplications.Inrecentyears,thescale-invariantfeaturetransform(SIFT)algorithmhasgainedsignificantpopularityduetoitsrobustnessagainstvariationsinlighting,pose,andocclusion.However,itstillfaceslimitationswhenappliedtofacerecognitiontasks,includinglow-dimensionalfeaturerepresentationandcomputationalcomplexity.Toaddresstheseissues,thispaperproposesanimprovedSIFT-basedfacerecognitionmethodcalledADSIFT.ADSIFTenhancesthediscriminativepowerofSIFTfeaturesbyincorporatingadaptiveselectionanddimensionalityreductiontechniques.ExperimentalresultsdemonstratethatADSIFTachievessuperiorperformancecomparedtoconventionalSIFTintermsofrecognitionaccuracyandcomputationspeed. 1.Introduction Facerecognitionhasbeenasubjectofextensiveresearchinrecentyears,owingtoitswiderangeofapplicationssuchassecuritysystems,identityverification,andsocialmedia.SIFT,alocalfeaturedescriptor,hasbeenwidelyusedinvariouscomputervisiontasksduetoitsremarkableinvarianceproperties.However,whenapplieddirectlytofacerecognition,SIFTencounterslimitationsduetoitshigh-dimensionalfeaturerepresentationandhighcomputationalcomplexity.ThispaperintroducesADSIFT,animprovedSIFT-basedfacerecognitionmethod,whichaddressestheselimitationsandenhancesthediscriminativepowerofSIFTfeatures. 2.TheSIFTAlgorithm TheSIFTalgorithminvolvesmultiplestages,includingkeypointdetection,orientationassignment,featuredescriptorextraction,andfeaturematching.ThekeypointdetectionstepidentifiesdistinctiveinterestpointsinanimageusingtheDifferenceofGaussian(DoG)scale-spacerepresentation.Orientationassignmentassignsadominantorientationtoeachkeypointbasedonthegradientorientationsinitslocalneighborhood.Featuredescriptorextractionconstructsadescriptorforeachkeypointbyconsideringthegradientmagnitudesandorientationsinitssurroundingregion.Finally,featurematchingcomparestheextractedfeaturedescriptorstofindcorrespondingk