文献翻译——使用Adaboost改进的快速pca算法的实时人脸识别

1外文文献资料InternationalJournalofArtificialIntelligence&Applications(IJAIA),Vol.2,No.3,July2011DOI:10.5121/ijaia.2011.230545RealtimefacerecognitionusingadaboostimprovedfastpcaalgorithmABSTRACTThispaperpresentsanautomatedsystemforhumanfacerecognitioninarealtimebackgroundworldforalargehomemadedatasetofpersonsface.Thetaskisverydifficultastherealtimebackgroundsubtractioninanimageisstillachallenge.Additiontothisthereisahugevariationinhumanfaceimageintermsofsize,poseandexpression.Thesystemproposedcollapsesmostofthisvariance.TodetectrealtimehumanfaceAdaBoostwithHaarcascadeisusedandasimplefastPCAandLDAisusedtorecognizethefacesdetected.Thematchedfaceisthenusedtomarkattendanceinthelaboratory,inourcase.Thisbiometricsystemisarealtimeattendancesystembasedonthehumanfacerecognitionwithasimpleandfastalgorithmsandgainingahighaccuracyrate.KEYWORDSFacerecognition,Eigenface,AdaBoost,HaarCascadeClassifier,PrincipalComponentAnalysis(PCA),FastPCA,LinearDiscriminantAnalysis(LDA).1.INTRODUCTIONOverthelasttenyearsorso,facerecognitionhasbecomeapopularareaofresearchincomputervision.Facerecognitionisalsooneofthemostsuccessfulapplicationsofimageanalysisandunderstanding.Becauseofthenatureoftheproblemoffacerecognition,notonlycomputerscienceresearchersareinterestedinit,butneuroscientistsandpsychologistsarealsointerestedforthesame.Itisthegeneralopinionthatadvancesincomputervisionresearchwillprovideusefulinsightstoneuroscientistsandpsychologistsintohowhumanbrainworks,andvice2versa.Thetopicofrealtimefacerecognitionforvideoandcomplexreal-worldenvironmentshasgarneredtremendousattentionforstudenttoattendclassdailymeansonlineattendancesystemaswellassecuritysystembasedonfacerecognition.Automatedfacerecognitionsystemisabigchallengingproblemandhasgainedmuchattentionfromlastfewdecades.Therearemanyapproachesinthisfield.Manyproposedalgorithmsaretheretoidentifyandrecognizehumanbeingfaceformgivendataset.Therecentdevelopmentinthisfieldhasfacilitateduswithfastprocessingcapacityandhighaccuracy.Theeffortsarealsogoinginthedirectiontoincludelearningtechniquesinthiscomplexcomputervisiontechnology.Therearemanyexistingsystemstoidentifyfacesandrecognizedthem.Butthesystemsarenotsoefficienttohaveautomatedfacedetection,identificationandrecognition.Alotofresearchworkisgoinginthisdirectiontoincreasethevisualpowerofcomputer.Hence,thereisalotofscopeinthedevelopmentofvisualandvisionsystem.Buttherearedifficultiesinthepathsuchasdevelopmentofefficientvisualfeatureextractingalgorithmsandhighprocessingpowerforretrievalfromahugeimagedatabase.Asimageisacomplexhighdimension(3D)matrixandprocessingmatrixoperationisnotsofastandperfect.Hence,thisdirectionustohandlewithahugeimagedatabaseandfocusonthenewalgorithmswhicharemorereal-timeandmoreefficientwithmaximumpercentageofaccuracy.Efficientandeffectiverecognitionofhumanfacefromimagedatabasesisnowarequirement.Facerecognitionisabiometricmethodforidentifyingindividualsbytheirfeaturesofface.Applicationsoffacerecognitionarewidelyspreadinginareassuchascriminalidentification,securitysystem,imageandfilmprocessing.Fromthesequenceofimagecapturedbythecapturingdevice,inourcasecamera,thegoalistofindthebestmatchinthedatabase.Usingpre-storagedatabasewecanidentifyorverifyoneormoreidentitiesinthescene.Thegeneralblockdiagramforfacerecognitionsystemishavingthreemainblocks,thefirstisfacedetection,secondisfaceextractionandthethirdfacerecognition.Thebasicoverallfacerecognitionmodellooksliketheonebelow,infigure1.3Differentapproachesoffacerecognitionforstillimagescanbecategorizedintotreemaingroupssuchasholisticapproach,feature-basedapproach,andhybridapproach1.Facerecognitionformastillimagecanhavebasicthreecategories,suchasholisticapproach,feature-basedapproachandhybridapproach2.1.1HolisticApproach:-Inholisticapproach,thewholefaceregionistakenasaninputinfacedetectionsystemtoperformfacerecognition.1.2Feature-basedApproach:-Infeature-basedapproach,localfeaturesonfacesuchasnoseandeyesaresegmentedandthengiventothefacedetectionsystemtoeasierthetaskoffacerecognition.1.3HybridApproach:-Inhybridapproach,bothlocalfeaturesandthewholefaceisusedastheinputtothefacedetectionsystem.Itismoresimilartothebehaviourorhumanbeingtorecognizetheface.Thispaperisdividedintosevensections.Thefirstsectionistheintroductionpart;thesecondsectionisaproblemstatement;thethirdsectionfacerecognitiontechniques-literaturereview;thefourthsectionistheproposedmethodforfeatureextractionformafaceimagedataset,thefifthdivisionisabouttheimplementation;thesecondlastsectionshowstheresults;andthelastistheconclusionsection.2.PROBLEMSTATMENTThedifficultiesinfacerecognitionareveryreal-timeandnatural.Thefaceimagecanhaveheadposeproblem,illuminationproblem,facialexpressioncanalsobeabigproblem.Hairstyleandagingproblemcanalsoreducetheaccuracyofthesystem.Therecanbemanyotherproblemssuchasocclusion,i.e.,glass,scarf,etc.,thatcandecreasetheperformance.Imageisamulti-dimensionmatrixinmathematicsthatcanberepresentedbyamatrixvalue.Imagecanbetreatedasavectorhavingmagnitudeanddirectionboth.ItisknownasvectorimageorimageVector.4Ifrepresentsapxqimagevectorandxismatrixofimagevector.Thus,imagematrixcanberepresentedaswheretistransposeofthematrixx.Thus,toidentifytheglassinanimagematrixisverydifficultandrequiressomenewapproachesthatcanovercometheselimitations.Thealgorithmproposedinthispapersuccessfullyovercomestheselimitations.Butbeforethatletsseewhatalltechniqueshavebeenusedinthefieldoffaceidentificationandfacerecognition.3.FACERECOGNITIONTECHNIQUES3.1.FacedetectionFacedetectionisatechnologytodeterminethelocationsandsizeofahumanbeingfaceinadigitalimage.Itonlydetectsfacialexpressionandrestallintheimageistreatedasbackgroundandissubtractedfromtheimage.Itisaspecialcaseofobject-classdetectionorinmoregeneralcaseasfacelocalizer.Face-detectionalgorithmsfocusedonthedetectionoffrontalhumanfaces,andalsosolvethemulti-viewfacedetectionproblem.Thevarioustechniquesusedtodetectthefaceintheimageareasbelow:3.1.1.Facedetectionasapattern-classificationtask:Inthisfacedetectionisabinary-patternclassificationtask.Thatis,thecontentofagivenpartofanimageistransformedintofeatures,afterwhichaclassifiertrainedonexamplefacesdecideswhetherthatparticularregionoftheimageisaface,ornot.Controlledbackground:Inthistechniquethebackgroundisstillorisfixed.Removethebackgroundandonlythefaceswillbeleft,assumingtheimageonlycontainsafrontalface.Bycolor:Thistechniqueisvulnerable.Inthisskincolorisusedtosegmentthecolorimagetofindthefaceintheimage.Butthishassomedrawback;thestillbackgroundofthesamecolorwillalsobesegmented.3.1.4.Bymotion:Thefaceintheimageisusuallyinmotion.Calculatingthemovingareawill5getthefacesegment3.Butthistoohavemanydisadvantagesastheremaybebackgroundswhichareinmotion.3.1.5.Model-based:Afacemodelcancontaintheappearance,shape,andmotionoffaces3.Thistechniqueusesthefacemodeltofindthefaceintheimage.Someofthemodelscanberectangle,round,square,heart,andtriangle.Itgiveshighlevelofaccuracyifusedwithsomeothertechniques.3.2.FaceRecognitionFacerecognitionisatechniquetoidentifyapersonfacefromastillimageormovingpictureswithagivenimagedatabaseoffaceimages.Facerecognitionisbiometricinformationofaperson.However,faceissubjecttolotsofchangesandismoresensitivetoenvironmentalchanges.Thus,therecognitionrateofthefaceislowthantheotherbiometricinformationofapersonsuchasfingerprint,voice,iris,ear,palmgeometry,retina,etc.Therearemanymethodsforfacerecognitionandtoincreasetherecognitionrate.Someofthebasiccommonlyusedfacerecognitiontechniquesareasbelow:3.2.1.NeuralNetworksAneuralnetworklearningalgorithmcalledBackpropagationisamongthemosteffectiveapproachestomachinelearningwhenthedataincludescomplexsensoryinputsuchasimages,inourcasefaceimage.Neuralnetworkisanonlinearnetworkaddingfeaturestothelearningsystem.Hence,thefeaturesextractionstepmaybemoreefficientthanthelinearKarhunen-Loevemethodswhichchoseadimensionalityreducinglinearprojectionthatmaximizesthescatterofallprojectedsamples3.Thishasclassificationtimelessthan0.5seconds,buthastrainingtimemorethanhourorhours.However,whenthenumberofpersonsincreases,thecomputingexpensewillbecomemoredemanding5.Ingeneral,neuralnetworkapproachesencounterproblemswhenthenumberofclasses,i.e.,individualsincreases.3.2.2.GeometricalFeatureMatchingThistechniqueisbasedonthesetofgeometricalfeaturesfromtheimageofaface.Theoverallconfigurationcanbedescribedbyavectorrepresentingthe6positionandsizeofthemainfacialfeatures,suchaseyesandeyebrows,nose,mouth,andtheshapeoffaceoutline5.OneofthepioneeringworksonautomatedfacerecognitionbyusinggeometricalfeatureswasdonebyT.Kanade5.Theirsystemachievedapeakperformanceof75%recognitionrateonadatabaseof20peopleusingtwoimagesperperson,oneasthemodelandtheotherasthetestimage4.I.J.Coxel6introducedamixture-distancetechniquewhichachieved95%recognitionrateonaquerydatabaseof685individuals.Inthis,eachofthefacewasrepresentedby30manuallyextracteddistances.Firstthematchingprocessutilizedtheinformationpresentedinatopologicalgraphicsrepresentationofthefeaturepoints.Thenthesecondwillafterthatwillbecompensatingforthedifferentcenterlocation,twocostvalues,thatare,thetopologicalcost,andsimilaritycost,wereevaluated.Inshort,geometricalfeaturematchingbasedonpreciselymeasureddistancesbetweenfeaturesmaybemostusefulforfindingpossiblematchesinalargedatabase.GraphMatchingGraphmatchingisanothermethodusedtorecognizeface.M.Ladesetal7presentedadynamiclinkstructurefordistortioninvariantobjectrecognition,whichemployedelasticgraphmatchingtofindthecloseststoredgraph.Thisdynamiclinkisanextensionoftheneuralnetworks.Facearerepresentedasgraphs,withnodespositionedatfiducialpoints,(i.e.,exes,nose,),andedgeslabeledwithtwodimension(2-D)distancevector.Eachnodecontainsasetof40complexGaborwaveletcoefficientsatdifferentscalesandorientations(phase,amplitude).Theyarecalledjets.Recognitionisbasedonlabeledgraphs8.AjetdescribesasmallpatchofgreyvaluesinanimageI(x)aroundagivenpixelx=(x;y).Eachislabeledwithjetandeachedgeislabeledwithdistance.Graphmatching,thatis,dynamiclinkissuperiortoallotherrecognitiontechniquesintermsoftherotationinvariance.Butthematchingprocessiscomplexandcomputationallyexpensive.3.2.4.EigenfacesEigenfaceisaoneofthemostthoroughlyinvestigatedapproachestofacerecognition4.ItisalsoknownasKarhunen-Loeveexpansion,eigenpicture,eigenvector,andprincipalcomponent.L.SirovichandM.Kirby9,10used7principalcomponentanalysistoefficientlyrepresentpicturesoffaces.Anyfaceimagecouldbeapproximatelyreconstructedbyasmallcollectionofweightsforeachfaceandastandaredfacepicture,thatis,eigenpicture.Theweightsherearetheobtainedbyprojectingthefaceimageontotheeigenpicture.Inmathematics,eigenfacesarethesetofeigenvectorsusedinthecomputervisionproblemofhumanfacerecognition.Theprincipalcomponentsofthedistributionoffaces,ortheeigenvectorsofthecovariancematrixofthesetoffaceimageistheeigenface.Eachfacecanberepresentedexactlybyalinearcombinationoftheeigenfaces4.ThebestMeigenfacesconstructanMdimension(M-D)spacethatiscalledthe“facespace”whichissameastheimagespacediscussedearlier.Illuminationnormalization10isusuallynecessaryfortheeigenfacesapproach.L.ZhaoandY.H.Yang12proposedanewmethodtocomputethecovariancematrixusingthreeimageseachwastakenindifferentlightingconditionstoaccountforarbitraryilluminationeffects,iftheobjectisLambertianA.Pentland,B.Moghaddam13extendedtheirearlyworkoneigenfacetoeigenfeaturescorrespondingtofacecomponents,suchaseyes,nose,mouth.Eigenfeaturescombinesfacialmetrics(measuringdistancebetweenfacialfeatures)withtheeigenfaceapproach11.Thismethodoffacerecognitionisnotmuchaffectedbythelightingeffectandresultssomewhatsimilarresultsindifferentlightingconditions.3.2.5.FisherfaceBelhumeuretal14proposefisherfacesmethodbyusingPCAandFisherslineardiscriminantanalysistopropducesubspaceprojectionmatrixthatisverysimilartothatoftheeigenspacemethod.Itisoneofthemostsuccessfulwidelyusedfacerecognitionmethods.Thefisherfacesapproachtakesadvantageofwithin-classinformation;minimizingvariationwithineachclass,yetmaximizingclassseparation,theproblemwithvariationsinthesameimagessuchasdifferentlightingconditionscanbeovercome.However,Fisherfacerequiresseveraltrainingimagesforeachface,soitcannotbeappliedtothefacerecognitionapplicationswhereonlyoneexampleimageperpersonisavailablefortraining.3.3.FeatureExtractionTechniquesFacialfeatureextractionisnecessaryforidentificationofanindividualfaceonacomputer.Asfacialfeatures,theshapeoffacialpartsisautomatically8extractedfromafrontalfaceimage.Therecanbethreemethodsforthefacialfeatureextractionasgivenbelow:3.3.1.Geometry-basedThistechniqueisprosedbyKanada15theeyes,themouthandthenosebasearelocalizedusingtheverticaledgemap.Thesetechniquesrequirethreshold,which,giventheprevailingsensitivity,mayadverselyaffecttheachievedperformance.3.3.2.Template-basedThistechnique,matchesthefacialcomponentstopreviouslydesignedtemplatesusingappropriateenergyfunctional.Geneticalgorithmshavebeenproposedformoreefficientsearchingtimesintemplatematching.3.3.3.ColorsegmentationtechniquesThistechniquemakesuseofskincolortoisolatethefacialandnon-facialpartintheimage.Anynon-skincolorregionwithinthefaceisviewedasacandidateforeyesandormouth.Researchandexperimentsonfacerecognitionstillcontinuingsincemanydecadesbutstillthereisnosinglealgorithmperfectinrealtimefacerecognitionwithallthelimitationsdiscussedinsecondsection.Here,inthispaper,anewapproachisproposedtosomewhatovercomethelimitationswithaverylesscomplexity.4.FACIALFEATUREEXTRACTIONInmanyproblemdomainscombiningmorethanonetechniquewithanyothertechnique(s)oftenresultsinimprovementoftheperformance.Boostingisoneofsuchtechniqueusedtoincreasetheperformanceresult.Facialfeaturesareveryimportantinfacerecognition.Facialfeaturescanbeofdifferenttypes:region16,17,keypoint(landmark)18,19,andcontour20,21.Inthispaper,AdaBoost:BoostingalgorithmwithHaarCascadeClassifierforfacedetectionandfastPCAandPCAwithLDAforthepurposeoffacerecognition.Allthesealgorithmsareexplainedonebyone.4.1.FaceDetection4.1.1.AdaBoost:TheBoostingAlgorithm9AdaBoostisusedasashortformforAdaptiveBoosting,whichisawidelyusedmachinelearningalgorithmandisformulatedbyYoavFreundandRobertSchapire.Itsametaalgorithm,algorithmofalgorithm,andisusedinconjunctionwithotherlearningalgorithmstoimprovetheirperformanceofthatalgorithm(s)24.InourcaseabaBoostiscombinedwithhaarfeaturetoimprovetheperformancerate.Thealgorithm,AdaBoostisanadaptivealgorithminthesensethatthesubsequentclassifiersbuiltistweakedinfavorofinstancesofthosemisclassifiedbythepreviousclassifiers.Butitisverysensitivetonoisedataandtheoutliers.AdaBoosttakesaninputasatrainingsetS=(x1,y1).,(xm,ym),whereeachinstanceofS,xi,belongstoadomainorinstancespaceX,andsimilarlyeachlabelyibelongstothefinitelabelspace,thatisY.Hereinthispaper,weonlyfocusonthebinarycasewhenY=-1,+1.Thebasicideaofboostingisactuallytousetheweaklearnerofthefeaturescalculated,toformahighlycorrectpredictionrulesbycallingtheweaklearnerrepeatedlyprocessedonthedifferent-differentdistributionsoverthetrainingexamples.4.1.2.HaarCascadeClassifierAHaarClassifierisalsoamachinelearningalgorithmicapproachforthevisualobjectdetection,originallygivenbyViola&Jones23.Thistechniquewasoriginallyintendedforthefacialrecognitionbutitcanbeusedforanyotherobject.ThemostimportantfeatureoftheHaarClassifieristhat,itquicklyrejectsregionsthatarehighlyunlikelytobecontainedintheobject.ThecorebasisforHaarcascadeclassifierobjectdetectionistheHaar-likefeatures.Thesefeatures,ratherthanusingtheintensityvaluesofapixel,usethechangeincontrastvaluesbetweenadjacentrectangulargroupsofpixels25.Thevarianceofcontrastbetweenthepixelgroupsareusedtodeterminerelativelightanddarkareas.ThevariousHaar-like-featuresareshowninthefigure2.a.ThesetofbasicHaar-like-featureisshowninfigure2.b,rotatingwhichtheotherfeaturescanbegenerated.ThevalueofaHaar-likefeatureisthedifferencebetweenthesumofthepixelgraylevelvalueswithintheblackandwhiterectangularregions,i.e.,f(x)=Sumblackrectangle(pixelgraylevel)Sumwhiterectangle(pixelgraylevel)10Comparingwiththerawpixelvalues,Haar-likefeaturescanreduce/increasethein-class/out-ofclassvariability,andthusmakingclassificationmucheasier.TherectangleHaar-likefeaturescanbecomputedrapidlyusing“integralimage”.Integralimageatlocationofx,ycontainsthesumofthepixelvaluesaboveandleftofx,y,inclusive:Thesumofpixelvalueswithin“D”:UsingthisHaar-likefeaturesthefacedetectioncascadecanbedesignedasinthefigure4,below.InthisHaarcascadeclassifieranimageisclassifiedasahumanfaceifitpassesalltheconditions,f1,f2,fn.Ifatanystageanyofoneormoreconditionsisfalsethentheimagedoesnotcontainthehumanface.11Figure4.Thecascadeclassifierclassifiedfaceandnon-face.4.2.FaceRecognition4.2.1.PCAandFastPCA(PrincipalComponentAnalysis)Facerecognitionisoneofthenonintrusivebiometrictechniquescommonlyusedforverificationandauthentication.Localandglobalfeatures26basedextractiontechniquesareavailableforfacerecognition.Globalfeaturesextractiontechniquecalculatesco-variancematrixofinterimages27whereasauto-correlationmatrixiscomputedinlocalfeaturestechnique.PCAisamathematicalprocedurethattransformsanumberofpossiblycorrelatedvariablesintoasmallernumberofuncorrelatedvariablescalledprincipalcomponents.PCAcanbeexpressedingeneralas“alineartransformationoftheimagevectortotheprojectionfeaturevector”asgivenbelow:Y=X,where,WisthetransformationmatrixhavingdimensionKx1,YistheKxNfeaturevectormatrixandXisthehigherdimensionfacevectorobtainedbyrepresentingallthefaceimagesintoasinglevectorX=x1,x2,x3,.xNWhere,eachisafacevectorofdimension“n”obtainedfromtheMxNdimensionfaceimage28.Table1.FastPCAalgorithmforcomputingleadingeigenvectors22124.2.1LDALinearDiscriminantAnalysis(LDA)findsthevectorsintheunderlyingspacethatbestdiscriminateamongclasses.Forallsamplesofallclassesthebetween-classscattermatrixSTandthewithin-classscattermatrixSWaredefined.ThegoalistomaximizeSTwhileminimizingSW,inotherwords,maximizetheratiodet|ST|/det|SW|.Thisratioismaximizedwhenthecolumnvectorsoftheprojectionmatrixaretheeigenvectorsof(SW-1ST).Thescattermatricesarewhere,Cisthenumberofdistinctclasses,Nisthenumberofimagesforeachclassesi,th