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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,RESEARCHERSHAVEINVESTIGATEDTHEUSEDATAFUSIONMETHODWHICHCOMBINESDIFFERENTTYPESOFDATAGATHEREDBYTHESIMULTANEOUSUSEOFSEVERALSENSINGMODALITIESTOGENERATEANEWTYPEOFDATAVARIOUSPERCEPTUALMECHANISMSINTEGRATETHESESENSESTOPRODUCEACADEMYPUBLISHERTHEINTERNALREPRESENTATIONOFTHESENSEDENVIRONMENTTHEINTEGRATIONTENDSTOBESYNERGISTICINTHESCENETHATINFORMATIONINFERREDFROMTHEPROCESSCANNOTBEOBTAINEDFROMANYPROPERSUBSETOFTHESENSEMODALITIESTHISPROPERTYOFSYNERGISMISONETHATSHOULDBESOUGHTFORWHENIMPLEMENTINGMULTISENSORINTEGRATIONFORMACHINEPERCEPTIONTHEPRINCIPALMOTIVATIONFORTHEFUSIONAPPROACHISTOEXPLOITSUCHSYNERGISMINTHETECHNIQUEFORCOMBINEDINTERPRETATIONOF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THATEACHFACEISREPRESENTEDBYAPAIROFIMAGES,ONEINTHEIRSPECTRUMANDONEINTHEVISIBLESPECTRUMBOTHIMAGESHAVEBEENCOMBINEDPRIORTOFUSIONTOENSURESIMILARRANGESOFVALUESWEFUSEDVISUALANDTHERMALIMAGESIDEALLY,THEFUSIONOFCOMMONPIXELSCANBEDONEBYPIXELWISEWEIGHTEDSUMMATIONOFVISUALANDTHERMALIMAGES9,ASBELOWFX,YAX,YVX,YBX,YTX,Y1WHEREFX,YISAFUSEDOUTPUTOFAVISUALIMAGE,VX,Y,ANDATHERMALIMAGE,TX,Y,WHILEAX,YANDBX,YREPRESENTTHEWEIGHTINGFACTORSFORVISUALANDTHERMALIMAGESRESPECTIVELYINTHISWORK,WEHAVECONSIDEREDAX,Y070ANDBX,Y030FIG2FUSIONTECHNIQUECEIGENFACESFORRECOGNITIONABCFIG3ATHERMALIMAGES,BVISUALIMAGES,CFUSEDIMAGESOFCORRESPONDINGTHERMALANDVISUALIMAGESINMATHEMATICALTERMS,WEWISHTOFINDPRINCIPALCOMPONENTS13,14,15OFTHEDISTRIBUTIONOFFACES,ORTHEEIGENVECTORSOFTHECOVARIANCEMATRIXOFTHESETOFFACEIMAGESTHESEEIGENVECTORSCANBETHOUGHTOFASSETOFFEATURESWHICHTOGETHERCHARACTERIZETHEVARIATIONSBETWEENFACEIMAGESEACHIMAGELOCATIONCONTRIBUTESMOREORLESSTOEACHEIGENVECTOR,SOTHATWECANDISPLAYTHEEIGENVECTORASSORTOFGHOSTLYFACEWHICHWECALLANEIGENFACEEACHFACEIMAGEINTHETRAININGSETCANBEPRESENTEDEXACTLYINTERMSOFALINEARCOMBINATIONOFTHEEIGENFACESTHENUMBEROFAPOSSIBLEEIGENFACESISEQUALTOTHENUMBEROFFACEIMAGESINTHETRAININGSETHOWEVERTHEFACESCANALSOBEAPPROXIMATEDUSINGONLYTHE“BEST“EIGENFACES,THOSETHATHAVETHELARGESTEIGENVALUESANDWHICHTHEREFOREACCOUNTFORTHEMOSTVARIANCEWITHINTHESETFACEIMAGESTHEBESTUEIGENFACESCONSTITUTEAUDIMENSIONALSUBSPACE,WHICHMAYBECALLEDAS“FACESPACE“OFALLPOSSIBLEIMAGESIDENTIFYINGIMAGESTHROUGHEIGENSPACEPROJECTIONTAKESTHREEBASICSTEPSFIRSTTHEEIGENSPACEMUSTBECREATEDUSINGTRAININGIMAGESAFTERTHATALLTHOSETRAININGIMAGESAREPROJECTEDINTOTHEEIGENSPACEANDCALLTHEMEIGENFACESTRAINACLASSIFIERUSINGTHESEEIGENFACESFINALLY,THETESTIMAGESAREIDENTIFIEDBYPROJECTINGTHEMINTOTHEEIGENSPACEANDCLASSIFYINGTHEMBYTHETRAINEDCLASSIFIERDANNUSINGBACKPROPAGATIONWITHMOMENTUMNEURALNETWORKS,WITHTHEIRREMARKABLEABILITYTODERIVEMEANINGFROMCOMPLICATEDORIMPRECISEDATA,CANBEUSEDTOEXTRACTPATTERNSANDDETECTTRENDSTHATARETOOCOMPLEXTOBENOTICEDBYEITHERHUMANSOROTHERCOMPUTERTECHNIQUESATRAINEDNEURALNETWORKCANBETHOUGHTOFASAN“EXPERT“INTHECATEGORYOFINFORMATIONITHASBEENGIVENTOANALYZETHEBACKPROPAGATIONLEARNINGALGORITHMISONEOFTHEMOSTHISTORICALDEVELOPMENTSINNEURALNETWORKSITHASREAWAKENEDTHESCIENTIFICANDENGINEERINGCOMMUNITYTOTHEMODELINGANDPROCESSINGACADEMYPUBLISHEROFMANYQUANTITATIVEPHENOMENAUSINGNEURALNETWORKSTHISLEARNINGALGORITHMISAPPLIEDTOMULTILAYERFEEDFORWARDNETWORKSCONSISTINGOFPROCESSINGELEMENTSWITHCONTINUOUSDIFFERENTIABLEACTIVATIONFUNCTIONSSUCHNETWORKSASSOCIATEDWITHTHEBACKPROPAGATIONLEARNINGALGORITHMAREALSOCALLEDBACKPROPAGATIONNETWORKSECLASSIFICATIONOFFUSEDEIGENFACESUSINGRADIALBASISFUNCTIONNETWORK16NEURALNETWORKSHAVEBEENEMPLOYEDANDCOMPAREDTOCONVENTIONALCLASSIFIERSFORANUMBEROFCLASSIFICATIONPROBLEMSTHERESULTSHAVESHOWNTHATTHEACCURACYOFTHENEURALNETWORKAPPROACHESISEQUIVALENTTOORSLIGHTLYBETTERTHANOTHERMETHODSALSO,DUETOTHESIMPLICITY,GENERALITYANDGOODLEARNINGABILITYOFTHENEURALNETWORKS,THESETYPESOFCLASSIFIERSAREFOUNDTOBEMOREEFFICIENTRADIALBASISFUNCTIONRBFNEURALNETWORKSAREFOUNDTOBEVERYATTRACTIVEFORMANYENGINEERINGPROBLEMSBECAUSE1THEYAREUNIVERSALAPPROXIMATES,2THEYHAVEAVERYCOMPACTTOPOLOGYAND3THEIRLEARNINGSPEEDISVERYFASTBECAUSEOFTHEIRLOCALLYTUNEDNEURONSANIMPORTANTPROPERTYOFRBFNEURALNETWORKSISTHATTHEYFORMAUNIFYINGLINKBETWEENMANYDIFFERENTRESEARCHFIELDSSUCHASFUNCTIONAPPROXIMATION,REGULARIZATION,NOISYINTERPOLATIONANDPATTERNRECOGNITIONTHEREFORE,RBFNEURALNETWORKSSERVEASANEXCELLENTCANDIDATEFORPATTERNCLASSIFICATIONWHEREATTEMPTSHAVEBEENCARRIEDOUTTOMAKETHELEARNINGPROCESSINTHISTYPEOFCLASSIFICATIONFASTERTHANNORMALLYREQUIREDFORTHEMULTILAYERFEEDFORWARDNEURALNETWORKS17INTHISPAPER,ANRBFNEURALNETWORKISUSEDASACLASSIFIERINAFACERECOGNITIONSYSTEMWHERETHEINPUTSTOTHENEURALNETWORKAREFEATUREVECTORSDERIVEDFROMTHEPROPOSEDFEATUREEXTRACTIONTECHNIQUEDESCRIBEDINIIBGEOMETRICALLY,THEKEYIDEAOFANRBFNEURALNETWORKISTOPARTITIONTHEINPUTSPACEINTOANUMBEROFSUBSPACESWHICHAREINTHEFORMOFHYPERSPHERESACCORDINGLY,CLUSTERINGALGORITHMSKMEANSCLUSTERING,FUZZYKMEANSCLUSTERINGANDHIERARCHICALCLUSTERINGWHICHAREWIDELYUSEDINRBFNEURALNETWORKS18,19AREALOGICALAPPROACHESTOINITIALCENTERS18,20HOWEVER,ITMAYBENOTEDTHATTHESECLUSTERINGAPPROACHESAREINHERENTLYUNSUPERVISEDLEARNINGALGORITHMSASNOCATEGORYINFORMATIONABOUTPATTERNSISUSEDASANILLUSTRATIVEEXAMPLE,CONSIDERASIMPLETRAININGSETXK,YKILLUSTRATEDINFIG4THEBLACKANDWHITEDATAPOINTSREFLECTTHECORRESPONDINGVALUESASSUMEDBYTHEDEPENDENTVARIABLEYKIFWESIMPLYUSEKMEANSCLUSTERINGAPPROACHWITHOUTCONSIDERINGYK,TWOEVIDENTCLUSTERSASSHOWNINFIG4AAREACHIEVEDTHISBRINGSABOUTSIGNIFICANTMISCLASSIFICATIONINITIALLYALTHOUGHTHECLUSTERINGBOUNDARIESAREMODIFIEDINTHESUBSEQUENTLEARNINGPHASE,THISCOULDEASILYLEADTOANUNDESIREDANDHIGHLYDOMINANTAVERAGINGPHENOMENONASWELLASTOMAKETHELEARNINGLESSEFFECTIVE19TOPRESERVEHOMOGENEOUSCLUSTERS,THREECLUSTERSASDEPICTEDINFIG4BSHOULDBECREATEDINOTHERWORDS,ASUPERVISEDCLUSTERINGPROCEDUREWHICHTAKESINTOCONSIDERATIONTHECATEGORYINFORMATIONOFTRAININGDATASHOULDBECONSIDEREDABFIG4TWODIMENSIONALPATTERNSANDCLUSTERINGACONVENTIONALCLUSTERING,BCLUSTERINGWITHHOMOGENEOUSANALYSISFIG5EFFECTOFGAUSSIANWIDTHSINCLUSTERINGWHILECONSIDERINGTHECATEGORYINFORMATIONOFTRAININGPATTERNS,ITSHOULDBEEMPHASIZEDTHATTHECLASSMEMBERSHIPSARENOTONLYDEPENDEDONTHEDISTANCEOFPATTERNS,BUTALSODEPENDEDONTHEGAUSSIANWIDTHSASILLUSTRATEDINFIG5,PISNEARTOTHECENTEROFCLASSKINEUCLIDEANDISTANCE,BUTWECANSELECTDIFFERENTGAUSSIANWIDTHSFOREACHCLUSTERSOTHATTHEPOINTPHASGREATERCLASSMEMBERSHIPTOCLASSJTHANTHATTOCLASSKTHEREFORE,THEUSEOFCLASSMEMBERSHIPIMPLIESTHATWESHOULDPROPOSEASUPERVISEDPROCEDURETOCLUSTERTHETRAININGPATTERNSANDDETERMINETHEINITIALGAUSSIANWIDTHSIIIEXPERIMENTALRESULTSANDDISCUSSIONSTHISWORKHASBEENSIMULATEDUSINGMATLAB7FORCOMPARISONOFRESULTSEXPERIMENTSARECONDUCTEDFORFUSEDIMAGESATHOROUGHSYSTEMPERFORMANCEINVESTIGATION,WHICHCOVERSALLCONDITIONSOFHUMANFACERECOGNITION,HASBEENCONDUCTEDTHEYAREFACERECOGNITIONUNDERIVARIATIONSINSIZE,IIVARIATIONSINCLASSJCLASSKPACADEMYPUBLISHERLIGHTINGCONDITIONS,IIIVARIATIONSINFACIALEXPRESSIONS,IVVARIATIONSINPOSEWEFIRSTANALYZETHEPERFORMANCEOFOURALGORITHMUSINGOTCBVSDATABASEWHICHISASTANDARDBENCHMARKTHERMALANDVISUALFACEIMAGESFORFACERECOGNITIONTECHNOLOGIESAOTCBVSDATABASEOUREXPERIMENTSWEREPERFORMONTHEFACEDATABASEWHICHISOBJECTTRACKINGANDCLASSIFICATIONBEYONDVISIBLESPECTRUMOTCBVSBENCHMARKDATABASECONTAINSASETOFTHERMALANDVISUALFACEIMAGESTHEREARE700IMAGESOFVISUALAND700THERMALIMAGESOF16DIFFERENTPERSONSFORSOMESUBJECT,THEIMAGESWERETAKENATDIFFERENTTIMESWHICHCONTAINQUITEAHIGHDEGREEOFVARIABILITYINLIGHTING,FACIALEXPRESSIONOPEN/CLOSEDEYES,SMILING/NONSMILINGETC,POSEUPRIGHT,FRONTALPOSITIONETCANDFACIALDETAILSGLASSES/NOGLASSESALLTHEIMAGESWERETAKENAGAINSTADARKHOMOGENEOUSBACKGROUNDWITHTHESUBJECTSINANDUPRIGHT,FONTALPOSITION,WITHTOLERANCEFORSOMETILTINGANDROTATIONOFUPTO20DEGREETHEVARIATIONINSCALEISUPTOABOUT10ALLTHEIMAGESINTHEDATABASEBCLASSIFICATIONOFFUSEDEIGENFACESUSINGRADIALBASISFUNCTIONNEURALNETWORKANDMULTILAYERPERCEPTRONOUTOFTOTAL700THERMALANDVISUALIMAGES400IMAGESARETAKENOUTOFWHICH200ARETHERMALIMAGESAND200AREVISUALIMAGESCOMBININGTHESETHERMALANDVISUALIMAGESWEGET200FUSEDIMAGES100OFTHESEIMAGESAREUSEDASTRAININGSETANDREST100IMAGESARETAKENASTESTINGIMAGESTHETRAININGSETCONTAINS10CLASSESWHICHMEANTHATEACHCLASSHAS10IMAGESNOW5IMAGESFROMONEPARTICULARCLASSWHICHARENOTUSEDASATRAININGIMAGEAND5MOREIMAGESOFTHEOTHERCLASSESARETAKENFROMTHETESTINGSETACCORDINGTOTHISPROCESSFORALLTHE10CLASSESWEGETTHERESULTSFORBOTHTHECASESIE,FORCLASSIFICATIONOFFUSEDEIGENFACESUSINGRBFNEURALNETWORKANDUSINGMULTILAYERPERCEPTRONNEURALNETWORKWHICHARESHOWNINFIG6BELOWFIG6COMPARISONBETWEENRBFANDMLPCLASSIFIERSFIG710NUMBERSOFFUSEDIMAGESUSEDASTHETESTINGSETOFCLASS1WHICHISNOTUSEDINTRAININGFIG810NUMBERSOFFUSEDIMAGESUSEDASTHETESTINGSETOFCLASS2WHICHISNOTUSEDINTRAININGFIG910NUMBERSOFFUSEDIMAGESUSEDASTHETESTINGSETOFCLASS3WHICHISNOTUSEDINTRAININGINTHEGRAPHSHOWNINFIG6,THESOLIDLINESHOWSTHERESULTSOFEXPERIMENTSUSINGRBFNNANDTHEDASHEDLINESHOWSTHERESULTSOFEXPERIMENTSUSINGMLPNNSO,FROMTHEGRAPHWECANEASILYSAYTHATRBFNEURALNETWORKGIVESBETTERRESULTTHANMLPNEURALNETWORKINTHEABOVEFIGURESFROMFIG7TOFIG9WEHAVESHOWNTHEFUSEDIMAGESWHICHAREUSEDINTHETESTINGSETO
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