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1、Rowley,Baluja,andKanade:NeuralNetwork-BasedFaceDetection(PAMI,January1998)24Copyright1998IEEE.Personaluseofthismaterialispermitted.However,permissiontoreprint/republishthismaterialforadvertisingorpromotionalpur-posesorforcreatingnewcollectiveworksforresaleorredistribut
2、iontoserversorlists,ortoreuseanycopyrightedcomponentofthisworkinotherworksmustbeobtainedfromtheIEEE.NeuralNetwork-BasedFaceDetectionHenryA.Rowley,ShumeetBaluja,andTakeoKanadeAbstractRowley,Baluja,andKanade:NeuralNetwork-BasedFaceDetection(PAMI,January1998)24Wepresentan
3、euralnetwork-baseduprightfrontalfacedetectionsystem.Aretinallycon-nectedneuralnetworkexaminessmallwindowsofanimage,anddecideswhethereachwin-dowcontainsaface.Thesystemarbitratesbetweenmultiplenetworkstoimproveperformanceoverasinglenetwork.Wepresentastraightforwardproced
4、ureforaligningpositivefaceex-amplesfortraining.Tocollectnegativeexamples,weuseabootstrapalgorithm,whichaddsfalsedetectionsintothetrainingsetastrainingprogresses.Thiseliminatesthedifficulttaskofmanuallyselectingnonfacetrainingexamples,whichmustbechosentospantheentirespa
5、ceofnonfaceimages.Simpleheuristics,suchasusingthefactthatfacesrarelyoverlapinimages,canfurtherimprovetheaccuracy.Comparisonswithseveralotherstate-of-the-artfacedetec-tionsystemsarepresented;showingthatoursystemhascomparableperformanceintermsofdetectionandfalse-positive
6、rates.Keywords:Facedetection,Patternrecognition,Computervision,Artificialneuralnetworks,Ma-chinelearning1IntroductionInthispaper,wepresentaneuralnetwork-basedalgorithmtodetectupright,frontalviewsoffacesingray-scaleimages1.Thealgorithmworksbyapplyingoneormoreneuralnetwo
7、rksdirectlytoportionsoftheinputimage,andarbitratingtheirresults.Eachnetworkistrainedtooutputthepresenceorabsenceofaface.Thealgorithmsandtrainingmethodsaredesignedtobegeneral,withlittlecustomizationforfaces.Rowley,Baluja,andKanade:NeuralNetwork-BasedFaceDetection(PAMI,J
8、anuary1998)24Manyfacedetectionresearchershaveusedtheideathatfacialimagescanbecharacterizeddirectlyintermsofpixelinten
