人脸识别最新英文论文2

ACADEMYPUBLISHERIMAGEPIXELFUSIONFORHUMANFACERECOGNITIONMRINALKANTIBHOWMIK1,DEBOTOSHBHATTACHARJEE2,MITANASIPURI2,DIPAKKUMARBASU2,ANDMAHANTAPASKUNDU21DEPARTMENTOFCOMPUTERSCIENCEANDENGINEERING,TRIPURAUNIVERSITYSURYAMANINAGAR799130,TRIPURA,INDIAEMAILMKB_CSEYAHOOCOIN2DEPARTMENTOFCOMPUTERSCIENCEANDENGINEERING,JADAVPURUNIVERSITYKOLKATA700032,INDIAAICTEEMERITUSFELLOWEMAILDEBOTOSHINDIATIMESCOM,MITA_NASIPURI,DIPAKKBASUGMAILCOM,MKUNDUICSEJDVUACINABSTRACTINTHISPAPERWEPRESENTATECHNIQUEFORFUSIONOFOPTICALANDTHERMALFACEIMAGESBASEDONIMAGEPIXELFUSIONAPPROACHOUTOFSEVERALFACTORS,WHICHAFFECTFACERECOGNITIONPERFORMANCEINCASEOFVISUALIMAGES,ILLUMINATIONCHANGESAREASIGNIFICANTFACTORTHATNEEDSTOBEADDRESSEDTHERMALIMAGESAREBETTERINHANDLINGILLUMINATIONCONDITIONSBUTNOTVERYCONSISTENTINCAPTURINGTEXTUREDETAILSOFTHEFACESOTHERFACTORSLIKESUNGLASSES,BEARD,MOUSTACHEETCALSOPLAYACTIVEROLEINADDINGCOMPLICACIESTOTHERECOGNITIONPROCESSFUSIONOFTHERMALANDVISUALIMAGESISASOLUTIONTOOVERCOMETHEDRAWBACKSPRESENTINTHEINDIVIDUALTHERMALANDVISUALFACEIMAGESHEREFUSEDIMAGESAREPROJECTEDINTOANEIGENSPACEANDTHEPROJECTEDIMAGESARECLASSIFIEDUSINGARADIALBASISFUNCTIONRBFNEURALNETWORKANDALSOBYAMULTILAYERPERCEPTRONMLPINTHEEXPERIMENTSOBJECTTRACKINGANDCLASSIFICATIONBEYONDVISIBLESPECTRUMOTCBVSDATABASEBENCHMARKFORTHERMALANDVISUALFACEIMAGESHAVEBEENUSEDCOMPARISONOFEXPERIMENTALRESULTSSHOWTHATTHEPROPOSEDAPPROACHPERFORMSSIGNIFICANTLYWELLINRECOGNIZINGFACEIMAGESWITHASUCCESSRATEOF96AND9507FORRBFNEURALNETWORKANDMLPRESPECTIVELYINDEXTERMSIMAGEPIXELFUSION,EIGENSPACEPROJECTION,RADIALBASISFUNCTIONNEURALNETWORK,MULTILAYERPERCEPTRON,FACERECOGNITIONIINTRODUCTIONHUMANFACERECOGNITIONHASALREADYESTABLISHEDITSACCEPTANCEASASUPERIORBIOMETRICMETHODFORIDENTIFICATIONANDAUTHENTICATIONPURPOSESITISTOUCHLESS,HIGHLYAUTOMATEDANDMOSTNATURALSINCEITCOINCIDESWITHTHEMODEOFRECOGNITIONTHATWEASHUMANSEMPLOYONOUREVERYDAYAFFAIRS1ITHASEMERGEDASAPREFERREDALTERNATIVETOTRADITIONALFORMSOFIDENTIFICATION,LIKECARDIDS,WHICHARENOTEMBEDDEDINTOONESPHYSICALCHARACTERISTICSRESEARCHINTOSEVERALBIOMETRICMODALITIESINCLUDINGFACE,FINGERPRINT,IRIS,ANDRETINARECOGNITIONHASPRODUCEDVARYINGDEGREESOFSUCCESS2ITHASMANYPRACTICALAPPLICATIONS,SUCHASBANKCARDIDENTIFICATION,ACCESSCONTROL,MUGSHOTSSEARCHING,SECURITYMONITORING,SURVEILLANCESYSTEMSETC3,4,5MOSTOFTHERESEARCHEFFORTSINTHISAREAHAVEFOCUSEDONVISIBLESPECTRUMIMAGINGANDGEOMETRICFEATUREEXTRACTIONDESPITETHESUCCESSOFAUTOMATICFACERECOGNITIONTECHNIQUESINMANYPRACTICALAPPLICATIONS,THETASKOFFACERECOGNITIONBASEDONLYONTHEVISIBLESPECTRUMISSTILLACHALLENGINGPROBLEMUNDERUNCONTROLLEDENVIRONMENTSTHECHALLENGESAREEVENMOREPROFOUNDWHENONECONSIDERSTHELARGEVARIATIONSINTHEVISUALSTIMULUSDUETOILLUMINATIONCONDITIONS,VIEWINGDIRECTIONSORPOSES,FACIALEXPRESSIONS,AGING,ANDDISGUISESSUCHASFACIALHAIR,GLASSES,ORCOSMETICSINTHISCONNECTION,THEREARETWOMAJORCHALLENGESVARIATIONSINILLUMINATIONANDPOSE6SUCHPROBLEMSAREQUITEUNAVOIDABLEINAPPLICATIONSSUCHASOUTDOORACCESSCONTROLANDSURVEILLANCEPERFORMANCEOFVISUALFACERECOGNITIONISSENSITIVETOVARIATIONSINILLUMINATIONCONDITIONSANDUSUALLYDEGRADESSIGNIFICANTLYWHENTHELIGHTINGISDIMORWHENITISNOTUNIFORMLYILLUMINATINGTHEFACETHECHANGESCAUSEDBYILLUMINATIONONTHESAMEINDIVIDUALAREOFTENLARGERTHANTHEDIFFERENCESBETWEENINDIVIDUALSVARIOUSALGORITHMSEGHISTOGRAMEQUALIZATION,DROPPINGLEADINGEIGENFACESETCFORCOMPENSATINGSUCHVARIATIONSHAVEBEENSTUDIEDWITHPARTIALSUCCESSTHESETECHNIQUESATTEMPTTOREDUCETHEWITHINCLASSVARIABILITYINTRODUCEDBYCHANGESINILLUMINATIONAVISUALFACERECOGNITIONSYSTEMOPTIMIZEDFORIDENTIFICATIONOFLIGHTSKINNEDPEOPLECOULDBEPRONETOHIGHERFALSEALARMSAMONGDARKSKINNEDPEOPLETHERMALIRIMAGERY7HASBEENSUGGESTEDASAVIABLEALTERNATIVEINDETECTINGDISGUISEDFACESANDHANDLINGSITUATIONSWHERETHEREISNOCONTROLOVERILLUMINATIONTHERMALIRIMAGESREPRESENTTHEHEATPATTERNSEMITTEDFROMANOBJECTOBJECTSEMITDIFFERENTAMOUNTSOFIRENERGYACCORDINGTOTHEIRBODYTEMPERATUREANDCHARACTERISTICSSINCE,VESSELSTRANSPORTWARMBLOODTHROUGHOUTTHEBODYTHETHERMALPATTERNSOFFACESAREDERIVEDPRIMARILYFROMTHEPATTERNOFBLOODVESSELSUNDERTHESKINTHEVEINANDTISSUESTRUCTUREOFTHEFACEISUNIQUEFOREACHPERSON,ANDTHEREFORETHEIRIMAGESAREALSOUNIQUEITISKNOWNTHATEVENIDENTICALTWINSHAVEDIFFERENTTHERMALPATTERNSFACERECOGNITIONBASEDONTHERMALIRSPECTRUMUTILIZESTHEANATOMICALINFORMATIONOFHUMANFACEASFEATURESUNIQUETOEACHINDIVIDUALWHILESACRIFICINGCOLORRECOGNITIONANATOMICALFEATURESOFFACESUSEFULFORIDENTIFICATIONCANBEMEASUREDATADISTANCEUSINGPASSIVEIRSENSORTECHNOLOGYWITHORWITHOUTTHECOOPERATIONOFTHESUBJECT6RECENTLY,RESEARCHERSHAVEINVESTIGATEDTHEUSEDATAFUSIONMETHODWHICHCOMBINESDIFFERENTTYPESOFDATAGATHEREDBYTHESIMULTANEOUSUSEOFSEVERALSENSINGMODALITIESTOGENERATEANEWTYPEOFDATAVARIOUSPERCEPTUALMECHANISMSINTEGRATETHESESENSESTOPRODUCEACADEMYPUBLISHERTHEINTERNALREPRESENTATIONOFTHESENSEDENVIRONMENTTHEINTEGRATIONTENDSTOBESYNERGISTICINTHESCENETHATINFORMATIONINFERREDFROMTHEPROCESSCANNOTBEOBTAINEDFROMANYPROPERSUBSETOFTHESENSEMODALITIESTHISPROPERTYOFSYNERGISMISONETHATSHOULDBESOUGHTFORWHENIMPLEMENTINGMULTISENSORINTEGRATIONFORMACHINEPERCEPTIONTHEPRINCIPALMOTIVATIONFORTHEFUSIONAPPROACHISTOEXPLOITSUCHSYNERGISMINTHETECHNIQUEFORCOMBINEDINTERPRETATIONOFIMAGESOBTAINEDFROMMULTIPLESENSORSRESEARCHWORKONFUSIONHASBEENCARRIEDOUTONLYFORLASTFEWYEARSFUSIONMETHODSCANBECLASSIFIEDINTOTWOCATEGORIESASSTATEDIN8ONEISABOUTWEAKLYCOUPLEDFUSIONMETHODS,ANDTHEOTHERISABOUTSTRONGLYCOUPLEDFUSIONMETHODSINTHEFIRSTCATEGORYOFFUSIONMETHODS,FUSIONOFDATAPRODUCEDBYSENSORYMODULESDOESNOTAFFECTTHEOPERATIONOFTHEMODULESONTHECONTRARY,FORSTRONGLYCOUPLEDFUSIONMETHODS,THEMODULESPRODUCINGTHEDATATOBEFUSEDAREBEINGAFFECTEDINSOMEWAYBYOTHERINFORMATIONFROMOTHERMODULESTHEDETAILEDREVIEWONCURRENTADVANCESINVISUALANDTHERMALFACERECOGNITIONISGIVENIN9SIMPLEIMAGEFUSIONINSPATIALDOMAINISDISCUSSEDIN10,WHEREFACERECOGNITIONISUSEDFORTESTINGTHEFUSIONOFFACEDATABASEIMAGESBOTHIMAGEFUSIONANDDECISIONFUSIONISEMPLOYEDIN11TOIMPROVETHEACCURACYOFTHEFACERECOGNITIONSYSTEMINTHISPAPER,WEPRESENTANEWAPPROACHTOTHEPROBLEMOFFACERECOGNITIONTHATREALIZESTHEFULLPOTENTIALOFFUSIONOFTHERMALIRBANDANDVISUALBANDIMAGESINTHISWORKATFIRSTTHERMALANDVISUALFACEIMAGESARECOMBINEDTOGETHERANDFUSEDIMAGEOFCORRESPONDINGTHERMALANDVISUALFACEIMAGESAREOBTAINEDAFTERTHATUSINGTHESETRANSFORMEDFUSEDIMAGESEIGENFACESARECOMPUTEDANDFINALLYTHOSEEIGENFACESTHUSFOUNDARECLASSIFIEDUSINGARADIALBASISFUNCTIONNEURALNETWORKINTHEFIRSTCASEANDINTHESECONDCASETHEEIGENFACESARECLASSIFIEDUSINGMULTILAYERPERCEPTRONTHEORGANIZATIONOFTHERESTOFTHISPAPERISASFOLLOWSINSECTIONII,THEOVERVIEWOFTHESYSTEMISDISCUSSED,INSECTIONIIIEXPERIMENTALRESULTSANDDISCUSSIONSAREGIVENFINALLY,SECTIONIVCONCLUDESTHISWORKIITHESYSTEMOVERVIEWINTHISWORKWEHAVEUSEDOBJECTTRACKINGANDCLASSIFICATIONBEYONDVISIBLESPECTRUMOTCBVSDATABASEBENCHMARKTHERMALANDVISUALFACEIMAGESEVERYTHERMALFACEIMAGEANDTHECORRESPONDINGVISUALFACEIMAGEAREFIRSTCOMBINEDANDCONVERTEDINTOFUSEDIMAGETHESETRANSFORMEDIMAGESARESEPARATEDINTOTWOGROUPSNAMELYTRAININGANDTESTINGSETTHEEIGENSPACEISCOMPUTEDUSINGTRAININGIMAGESALLTHETRAININGANDTESTINGIMAGESAREPROJECTEDINTOTHECREATEDEIGENSPACEANDNAMEDASFUSEDEIGENFACESAFTERALLTHECONVERSIONSACLASSIFIERISUSEDTOCLASSIFYTHEMINFIRSTCASEARADIALBASISFUNCTIONNEURALNETWORKANDINSECONDCASEAMULTILAYERPERCEPTRONAREUSEDFORTHISPURPOSETHEBLOCKDIAGRAMOFTHESYSTEMISGIVENINFIG1INTHISFIGUREDOTTEDLINESINDICATEFEEDBACKFROMDIFFERENTSTEPSTOTHEIRFIG1BLOCKDIAGRAMOFTHEPRESENTSYSTEMPREVIOUSSTEPSTOIMPROVETHEEFFICIENCYOFTHESYSTEMSATHERMALINFRAREDFACEIMAGESTHERMALINFRAREDFACEIMAGESAREFORMEDASAMAPOFTHEMAJORBLOODVESSELSPRESENTINTHEFACETHEREFORE,AFACERECOGNITIONSYSTEMDESIGNEDBASEDONTHERMALINFRAREDFACEIMAGESCANNOTBEEVADEDORFOOLEDBYFORGERY,ORDISGUISE,ASCANOCCURUSINGTHEVISIBLESPECTRUMFORFACIALRECOGNITIONCOMPAREDTOVISUALFACERECOGNITIONSYSTEMSTHISRECOGNITIONSYSTEMWILLBELESSVULNERABLETOVARYINGCONDITIONS,SUCHASHEADANGLE,EXPRESSION,ORLIGHTINGBIMAGEFUSIONTECHNIQUETHETASKOFINTERPRETINGIMAGES,EITHERVISUALIMAGESALONEORTHERMALIMAGESALONE,ISANUNCONSTRAINTPROBLEMTHETHERMALIMAGECANATBESTYIELDESTIMATESOFSURFACETEMPERATURETHAT,INGENERAL,ISNOTSPECIFICINDISTINGUISHINGBETWEENOBJECTCLASSESTHEFEATURESEXTRACTEDFROMVISUALINTENSITYIMAGESALSOLACKTHESPECIFICITYREQUIREDFORUNIQUELYDETERMININGTHEIDENTITYOFTHEIMAGEDOBJECTTHEINTERPRETATIONOFEACHTYPEOFIMAGETHUSLEADSTOAMBIGUOUSINFERENCESABOUTTHENATUREOFTHEOBJECTSINTHESCENETHEUSEOFTHERMALDATAGATHEREDBYANINFRAREDCAMERA,ALONGWITHTHEVISUALIMAGE,ISSEENASAWAYOFRESOLVINGSOMEOFTHESEAMBIGUITIESONTHEOTHERHAND,THERMALIMAGESAREOBTAINEDBYSENSINGRADIATIONINTHEINFRAREDSPECTRUMTHERADIATIONSENSEDISEITHEREMITTEDBYANOBJECTATANONZEROABSOLUTETEMPERATURE,ORREFLECTEDBYITTHEMECHANISMSTHATPRODUCETHERMALANDVISUALIMAGESAREDIFFERENTFROMEACHOTHERTHERMALIMAGEPRODUCEDBYANOBJECTSSURFACECANBEINTERPRETEDTOIDENTIFYTHESEMECHANISMSTHUS,THERMALIMAGESCANPROVIDEINFORMATIONABOUTTHEOBJECTBEINGIMAGEDWHICHISNOTAVAILABLEFROMAVISUALIMAGE8AGREATDEALOFEFFORTHASBEENEXPENDEDONAUTOMATEDSCENEANALYSISUSINGVISUALIMAGES,ANDSOMEWORKHASVISUALIMAGEEIGENSPACEPROJECTIONCLASSIFICATIONUSINGRADIALBASISFUNCTIONNEURALNETWORK/MULTILAYERPERCEPTIONCLASSESRECOGNITIONRESULTSTHERMALIMAGEPIXELFUSIONACADEMYPUBLISHERBEENDONEINRECOGNIZINGOBJECTSINASCENEUSINGINFRAREDIMAGESHOWEVER,THEREHASBEENLITTLEEFFORTONINTERPRETINGTHERMALIMAGESOFOUTDOORSCENESBASEDONASTUDYOFTHEMECHANISMTHATGIVESRISETOTHEDIFFERENCESINTHETHERMALBEHAVIOROFOBJECTSURFACESINTHESCENEALSO,NORHASBEENANYEFFORTBEENMADETOINTEGRATEINFORMATIONEXTRACTEDFROMTHETWOMODALITIESOFIMAGINGINOURMETHODTHEPROCESSOFIMAGEFUSIONISWHEREPIXELDATAOF70OFVISUALIMAGEAND30OFTHERMALIMAGEOFSAMECLASSORSAMEIMAGEISBROUGHTTOGETHERINTOACOMMONOPERATINGIMAGEORNOWCOMMONLYREFERREDTOASACOMMONRELEVANTOPERATINGPICTURECROP12THISIMPLIESTHATANADDITIONALDEGREEOFFILTERINGANDINTELLIGENCEISTOBEAPPLIEDTOTHEPIXELSTREAMSTOPRESENTPERTINENTINFORMATIONTOTHEUSERSOIMAGEPIXELFUSIONHASTHECAPACITYTOENABLESEAMLESSWORKINGINAHETEROGENEOUSWORKENVIRONMENTWITHMORECOMPLEXDATAFORACCURATEANDEFFECTIVEFACERECOGNITIONWEREQUIREMOREINFORMATIVEIMAGESIMAGEBYONESOURCEIETHERMALMAYLACKSOMEINFORMATIONWHICHMIGHTBEAVAILABLEINIMAGESBYOTHERSOURCEIEVISUALSOIFITBECOMESPOSSIBLETOCOMBINETHEFEATURESOFBOTHTHEVISUALANDTHERMALFACEIMAGESTHENEFFICIENT,ROBUST,ANDACCURATEFACERECOGNITIONCANBEDEVELOPEDWEDESCRIBEBELOWINDETAILTHEFUSIONSCHEMECONSIDEREDINTHISWORKWEASSUME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IANWIDTHSINCLUSTERINGWHILECONSIDERINGTHECATEGORYINFORMATIONOFTRAININGPATTERNS,ITSHOULDBEEMPHASIZEDTHATTHECLASSMEMBERSHIPSARENOTONLYDEPENDEDONTHEDISTANCEOFPATTERNS,BUTALSODEPENDEDONTHEGAUSSIANWIDTHSASILLUSTRATEDINFIG5,PISNEARTOTHECENTEROFCLASSKINEUCLIDEANDISTANCE,BUTWECANSELECTDIFFERENTGAUSSIANWIDTHSFOREACHCLUSTERSOTHATTHEPOINTPHASGREATERCLASSMEMBERSHIPTOCLASSJTHANTHATTOCLASSKTHEREFORE,THEUSEOFCLASSMEMBERSHIPIMPLIESTHATWESHOULDPROPOSEASUPERVISEDPROCEDURETOCLUSTERTHETRAININGPATTERNSANDDETERMINETHEINITIALGAUSSIANWIDTHSIIIEXPERIMENTALRESULTSANDDISCUSSIONSTHISWORKHASBEENSIMULATEDUSINGMATLAB7FORCOMPARISONOFRESULTSEXPERIMENTSARECONDUCTEDFORFUSEDIMAGESATHOROUGHSYSTEMPERFORMANCEINVESTIGATION,WHICHCOVERSALLCONDITIONSOFHUMANFACERECOGNITION,HASBEENCONDUCTEDTHEYAREFACERECOGNITIONUNDERIVARIATIONSINSIZE,IIVARIATIONSINCLASSJCLASSKPACADEMYPUBLISHERLIGHTINGCONDITIONS,IIIVARIATIONSINFACIALEXPRESSIONS,IVVARIATIONSINPOSEWEFIRSTANALYZETHEPERFORMANCEOFOURALGORITHMUSINGOTCBVSDATABASEWHICHISASTANDARDBENCHMARKTHERMALANDVISUALFACEIMAGESFORFACERECOGNITIONTECHNOLOGIESAOTCBVSDATABASEOUREXPERIMENTSWEREPERFORMONTHEFACEDATABASEWHICHISOBJECTTRACKINGANDCLASSIFICATIONBEYONDVISIBLESPECTRUMOTCBVSBENCHMARKDATABASECONTAINSASETOFTHERMALANDVISUALFACEIMAGESTHEREARE700IMAGESOFVISUALAND700THERMALIMAGESOF16DIFFERENTPERSONSFORSOMESUBJECT,THEIMAGESWERETAKENATDIFFERENTTIMESWHICHCONTAINQUITEAHIGHDEGREEOFVARIABILITYINLIGHTING,FACIALEXPRESSIONOPEN/CLOSEDEYES,SMILING/NONSMILINGETC,POSEUPRIGHT,FRONTALPOSITIONETCANDFACIALDETAILSGLASSES/NOGLASSESALLTHEIMAGESWERETAKENAGAINSTADARKHOMOGENEOUSBACKGROUNDWITHTHESUBJECTSINANDUPRIGHT,FONTALPOSITION,WITHTOLERANCEFORSOMETILTINGANDROTATIONOFUPTO20DEGREETHEVARIATIONINSCALEISUPTOABOUT10ALLTHEIMAGESINTHEDATABASEBCLASSIFICATIONOFFUSEDEIGENFACESUSINGRADIALBASISFUNCTIONNEURALNETWORKANDMULTILAYERPERCEPTRONOUTOFTOTAL700THERMALANDVISUALIMAGES400IMAGESARETAKENOUTOFWHICH200ARETHERMALIMAGESAND200AREVISUALIMAGESCOMBININGTHESETHERMALANDVISUALIMAGESWEGET200FUSEDIMAGES100OFTHESEIMAGESAREUSEDASTRAININGSETANDREST100IMAGESARETAKENASTESTINGIMAGESTHETRAININGSETCONTAINS10CLASSESWHICHMEANTHATEACHCLASSHAS10IMAGESNOW5IMAGESFROMONEPARTICULARCLASSWHICHARENOTUSEDASATRAININGIMAGEAND5MOREIMAGESOFTHEOTHERCLASSESARETAKENFROMTHETESTINGSETACCORDINGTOTHISPROCESSFORALLTHE10CLASSESWEGETTHERESULTSFORBOTHTHECASESIE,FORCLASSIFICATIONOFFUSEDEIGENFACESUSINGRBFNEURALNETWORKANDUSINGMULTILAYERPERCEPTRONNEURALNETWORKWHICHARESHOWNINFIG6BELOWFIG6COMPARISONBETWEENRBFANDMLPCLASSIFIERSFIG710NUMBERSOFFUSEDIMAGESUSEDASTHETESTINGSETOFCLASS1WHICHISNOTUSEDINTRAININGFIG810NUMBERSOFFUSEDIMAGESUSEDASTHETESTINGSETOFCLASS2WHICHISNOTUSEDINTRAININGFIG910NUMBERSOFFUSEDIMAGESUSEDASTHETESTINGSETOFCLASS3WHICHISNOTUSEDINTRAININGINTHEGRAPHSHOWNINFIG6,THESOLIDLINESHOWSTHERESULTSOFEXPERIMENTSUSINGRBFNNANDTHEDASHEDLINESHOWSTHERESULTSOFEXPERIMENTSUSINGMLPNNSO,FROMTHEGRAPHWECANEASILYSAYTHATRBFNEURALNETWORKGIVESBETTERRESULTTHANMLPNEURALNETWORKINTHEABOVEFIGURESFROMFIG7TOFIG9WEHAVESHOWNTHEFUSEDIMAGESWHICHAREUSEDINTHETESTINGSETO