人脸识别最新英文论文1

MKBHOWMIK,DBHATTACHARJEE,MNASIPURI,DKBASUCTDVD11MINDABSTABSVCTDVD12MKBHOWMIK,DBHATTACHARJEE,MNASIPURI,DKBASUMKUNDUINTERNATIONALJOURNALOFIMAGEPROCESSINGIJIP,VOLUME4ISSUE16TOGENERATETHEFUSEDIMAGECOFCOEFFICIENTITWILLADDTHATVALUEWHICHISDEDUCTEDFROMTHEVISUALIMAGEDURINGTHECALCULATIONOFABSOLUTEVALUEDOFTHERMALTANDVISUALVIMAGEFORALLTHECOEFFICIENTSUSINGFUSIONMETHODSHOWNATEQUATION1112CONSEQUENTLYTHERECONSTRUCTIONPROCESSISPERFORMEDUSINGINVERSEOFWAVELETTRANSFORMATIONSTOGENERATESYNTHESIZEDFUSEDIMAGEXIDWT2CA,CH,CV,CD,WNAME13XIDWT2CA,CH,CV,CD,LO_R,HI_R14IDWTUSESTHEWAVELETWNAMETOCOMPUTETHESINGLELEVELRECONSTRUCTIONOFANIMAGEX,BASEDONAPPROXIMATIONMATRIXCAANDDETAILEDMATRICESCH,CVANDCDHORIZONTAL,VERTICALANDDIAGONALRESPECTIVELYBYTHEEQUATIONNO14,WECANRECONSTRUCTTHEIMAGEUSINGFILTERSLO_RRECONSTRUCTLOWPASSANDHI_RRECONSTRUCTHIGHPASSANDHAARANDDB2ASTHETIMEOFRECONSTRUCTIONOFANIMAGE33FIGURE2ASHOWNONEOFTHETHIRDLEVELDECOMPOSITIONOFLEVEL5BORIGINALIMAGEUSEDINTHEDECOMPOSITIONCORTHOGONALWAVELETREPRESENTATIONOFASAMPLEIMAGETHEDECOMPOSITIONPROCESSCANBEITERATED,WITHSUCCESSIVEAPPROXIMATIONSBEINGDECOMPOSEDINTURN,SOTHATONEIMAGEISBROKENDOWNINTOMANYLOWERRESOLUTIONCOMPONENTSTHISISCALLEDTHEWAVELETDECOMPOSITIONTREEINTHISWORK,DECOMPOSITIONWASDONEUPTOLEVELFIVEUSINGHAARANDDAUBECHIESDB2WAVELETTRANSFORMATIONS,ASSHOWNINFIGURE3AVERAGEOFCORRESPONDINGTRANSFORMCOEFFICIENTSFROMVISUALANDTHERMALIMAGESGIVESTHEMATRIXOFFUSEDCOEFFICIENTS,WHICHWHENTRANSFORMEDINTOTHEIMAGEINSPATIALDOMAINBYINVERSEWAVELETTRANSFORMPRODUCESFUSEDFACEIMAGETHESEFUSEDIMAGESTHUSFOUNDAREPASSEDTHROUGHPCAFORDIMENSIONALITYREDUCTION,WHICHISDESCRIBEDNEXTFIGURE3WAVELETDECOMPOSITIONTREEA3H3V3D3H2V2D2H1V1D1ABC3413423433446781234022232458687882185MKBHOWMIK,DBHATTACHARJEE,MNASIPURI,DKBASUMKUNDUINTERNATIONALJOURNALOFIMAGEPROCESSINGIJIP,VOLUME4ISSUE172BDIMENSIONALITYREDUCTIONPRINCIPALCOMPONENTANALYSISPCAISBASEDONTHESECONDORDERSTATISTICSOFTHEINPUTIMAGE,WHICHTRIESTOATTAINANOPTIMALREPRESENTATIONTHATMINIMIZESTHERECONSTRUCTIONERRORINALEASTSQUARESSENSEEIGENVECTORSOFTHECOVARIANCEMATRIXOFTHEFACEIMAGESCONSTITUTETHEEIGENFACESTHEDIMENSIONALITYOFTHEFACEFEATURESPACEISREDUCEDBYSELECTINGONLYTHEEIGENVECTORSPOSSESSINGSIGNIFICANTLYLARGEEIGENVALUESONCETHENEWFACESPACEISCONSTRUCTED,WHENATESTIMAGEARRIVES,ITISPROJECTEDONTOTHISFACESPACETOYIELDTHEFEATUREVECTORTHEREPRESENTATIONCOEFFICIENTSINTHECONSTRUCTEDFACESPACETHECLASSIFIERDECIDESFORTHEIDENTITYOFTHEINDIVIDUAL,ACCORDINGTOASIMILARITYSCOREBETWEENTHETESTIMAGESFEATUREVECTORANDTHEPCAFEATUREVECTORSOFTHEINDIVIDUALSINTHEDATABASE27,30,36,37,342CARTIFICIALNEURALNETWORKUSINGBACKPROPAGATIONWITHMOMENTUMNEURALNETWORKS,WITHTHEIRREMARKABLEABILITYTODERIVEMEANINGFROMCOMPLICATEDORIMPRECISEDATA,CANBEUSEDTOEXTRACTPATTERNSANDDETECTTRENDSTHATARETOOCOMPLEXTOBENOTICEDBYEITHERHUMANSOROTHERCOMPUTERTECHNIQUESATRAINEDNEURALNETWORKCANBETHOUGHTOFASAN“EXPERT”INTHECATEGORYOFINFORMATIONITHASBEENGIVENTOANALYZETHEBACKPROPAGATIONLEARNINGALGORITHMISONEOFTHEMOSTHISTORICALDEVELOPMENTSINNEURALNETWORKSITHASREAWAKENEDTHESCIENTIFICANDENGINEERINGCOMMUNITYTOTHEMODELINGANDPROCESSINGOFMANYQUANTITATIVEPHENOMENATHISLEARNINGALGORITHMISAPPLIEDTOMULTILAYERFEEDFORWARDNETWORKSCONSISTINGOFPROCESSINGELEMENTSWITHCONTINUOUSDIFFERENTIABLEACTIVATIONFUNCTIONSSUCHNETWORKSASSOCIATEDWITHTHEBACKPROPAGATIONLEARNINGALGORITHMAREALSOCALLEDBACKPROPAGATIONNETWORKS24,25,26,27,28,29,383EXPERIMENTALRESULTSANDDISCUSSIONSTHISWORKHASBEENSIMULATEDUSINGMATLAB7INAMACHINEOFTHECONFIGURATION213GHZINTELXEONQUADCOREPROCESSORAND1638400MBOFPHYSICALMEMORYWEANALYZETHEPERFORMANCEOFOURALGORITHMUSINGTHEIRISTHERMAL/VISUALFACEDATABASEINTHISDATABASE,ALLTHETHERMALANDVISIBLEUNREGISTEREDFACEIMAGESARETAKENUNDERVARIABLEILLUMINATIONS,EXPRESSIONS,ANDPOSESTHEACTUALSIZEOFTHEIMAGESIS320X240PIXELSFORBOTHVISUALANDTHERMALTOTAL30CLASSESAREPRESENTINTHATDATABASE31SOMETHERMALANDVISUALIMAGESANDTHEIRCORRESPONDINGFUSEDIMAGESFORHAARANDDAUBECHIESDB2WAVELETSARESHOWNINFIGURE4ANDFIGURE5RESPECTIVELYTOCOMPARERESULTSOFHAARANDDAUBECHIESDB2WAVELETS,FUSIONOFVISUALANDTHERMALIMAGESWEREDONESEPARATELYFORTHATPURPOSE200THERMALAND200VISUALIMAGESWERECONSIDEREDWEHAVEINCREASEDTHESIZEOFTESTINGIMAGEFORBOTHTHEWAVELETFIRSTLYWEHAVETESTEDOURSYSTEMUSING100FUSEDIMAGESUSINGTWODIFFERENTWAVELETANDWEINCREASE10IMAGESPERCLASSFOR10DIFFERENTCLASSESIETOTAL100IMAGETHEDATABASEISCATEGORIZEDINTO10CLASSESBASEDONCHANGESINILLUMINATIONANDEXPRESSIONSTHECLASSESWITHILLUMINATIONSANDEXPRESSIONCHANGESARECLASS1,CLASS2,CLASS3,CLASS4,CLASS6,CLASS7,ANDCLASS9,WHEREASCLASS5,CLASS8ANDCLASS10AREWITHCHANGESINEXPRESSIONSONLYBOTHTHETECHNIQUESWEREAPPLIEDANDEXPERIMENTALRESULTSTHUSOBTAINEDARESHOWNINFIGURE6AFTERINCREASINGTHENUMBEROFTESTINGIMAGESTHERECOGNITIONRATESAREINCREASEDFORBOTHTHEWAVELETSPREVIOUSLYWEHAVEAVERAGERECOGNITIONRATEIS85FORHAARWAVELETAND90019FORDAUBECHIESWAVELETWHENTHENUMBEROFTESTINGIMAGEIS100IE10IMAGEPERCLASSNOWWEHAVEAVERAGERECOGNITIONRATEIS87FORHAARAND915FORDAUBECHIESWAVELETAFTERINCREASETHENUMBEROFTESTINGIMAGE100TO200IE20IMAGEPERCLASSMKBHOWMIK,DBHATTACHARJEE,MNASIPURI,DKBASUMKUNDUINTERNATIONALJOURNALOFIMAGEPROCESSINGIJIP,VOLUME4ISSUE18FIGURE4SAMPLEIMA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