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1、HumanFaceRecognitionusingLineFeaturesMrinalKantiBhowmik1,DebotoshBhattacharjee2,MitaNasipuri2,DipakKumarBasu2,MahantapasKundu21DepartmentofComputerScienceandEngineering,TripuraUniversity,Suryamaninagar-799130,Tripura,India2DepartmentofComputerScienceandEngineering,J
2、adavpurUniversity,Kolkata-700032,IndiaABSTRACTInthisworkweinvestigateanovelapproachtohandlethechallengesoffacerecognition,whichincludesrotation,scale,occlusion,illuminationetc.Here,wehaveusedthermalfaceimagesasthosearecapabletominimizetheaffectofilluminationchangesa
3、ndocclusionduetomoustache,beards,adornmentsetc.Theproposedapproachregistersthetrainingandtestingthermalfaceimagesinpolarcoordinate,whichiscapabletohandlecomplicaciesintroducedbyscalingandrotation.Linefeaturesareextractedfromthermalpolarimagesandfeaturevectorsarecons
4、tructedusingtheseline.Featurevectorsthusobtainedpassesthroughprincipalcomponentanalysis(PCA)forthedimensionalityreductionoffeaturevectors.Finally,theimagesprojectedintoeigenspaceareclassifiedusingamulti-layerperceptron.IntheexperimentswehaveusedObjectTrackingandClas
5、sificationBeyondVisibleSpectrum(OTCBVS)database.Experimentalresultsshowthattheproposedapproachsignificantlyimprovestheverificationandidentificationperformanceandthesuccessrateis99.25%.Keywords:Facerecognition,Thermalfaceimages,Polartransformation,Principalcomponenta
6、nalysis,Linefeatures,Multilayerneuralnetwork,Backpropagationlearning,Classification1.INTRODUCTIONTobesafeandsecureinthepresentworlditisrequiredtoauthenticateanauthorizedperson,tokeepsuspectsunderconstantsurveillance,andalsotoinvestigateaboutcriminals,missingandtrace
7、lessindividualswithinareasonablecoststructureisamajorproblem.Asecuritysystembasedonhumanfaceisnon-intrusiveinnature,i.e.withoutanyconsentfromthepersonconcerned,he/shecanbeinvestigated,whichisnottrueforothertypesofbiometricsignatureslike,finger-print,handgesture,reti
8、naetc.Humanfaceimageanalysislacksrobustnessduetodifferentvariationandthesourcesarebeard,moustache,spectacles,hair,adornmentsetc.Thermalfac
