毕业论文外文翻译指纹识别和验证的匹配系.docx

毕业论文外文翻译指纹识别和验证的匹配系.docx

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毕业论文外文翻译指纹识别和验证的匹配系

外文翻译

毕业设计题目：

指纹识别与研究

原文：

FingerprintIdentificationand

VerificationSystemusing

MinutiaeMatching

译文：

指纹识别和验证的匹配系统

原文：

FingerprintIdentificationandVerificationSystemusingMinutiaeMatching

Abstract:

Fingerprintsarethemostwidelyusedbiometricfeatureforpersonidentificationandverificationinthefieldofbiometricidentification.Fingerprintspossesstwomaintypesoffeaturesthatareusedforautomaticfingerprintidentificationandverification:

（i）globalridgeandfurrowstructurethatformsaspecialpatterninthecentralregionofthefingerprintand（ii）Minutiaedetailsassociatedwiththelocalridgeandfurrowstructure.ThispaperpresentstheimplementationofaminutiaebasedapproachtofingerprintidentificationandverificationandservesasareviewofthedifferenttechniquesusedinvariousstepsinthedevelopmentofminutiaebasedAutomaticFingerprintIdentificationSystems（AFIS）.Thetechniqueconferredinthispaperisbasedontheextractionofminutiaefromthethinned,binarizedandsegmentedversionofafingerprintimage.Thesystemusesfingerprintclassificationforindexingduringfingerprintmatchingwhichgreatlyenhancestheperformanceofthematchingalgorithm.Goodresults（~92%accuracy）wereobtainedusingtheFVC2000fingerprintdatabases.

1.INTRODUCTION

Fingerprintshavebeeninuseforbiometricrecognitionsincelongbecauseoftheirhighacceptability,immutabilityandindividuality.Immutabilityreferstothepersistenceofthefingerprintsovertimewhereasindividualityisrelatedtotheuniquenessofridgedetailsacrossindividuals.Theprobabilitythattwofingerprintsarealikeis1in1.9x1015[1].Thesefeaturesmaketheuseoffingerprintsextremelyeffectiveinareaswheretheprovisionofahighdegreeofsecurityisanissue.Themajorstepsinvolvedinautomatedfingerprintrecognitionincludea）FingerprintAcquisition,b）FingerprintSegmentation,c）FingerprintImageEnhancement,d）FeatureExtractione）MinutiaeMatching,f）FingerprintClassification.

Fingerprintacquisitioncaneitherbeoffline（inked）orOnline（Livescan）.Intheinkedmethodanimprintofaninkedfingerisfirstobtainedonapaper,whichisthenscanned.Thismethodusuallyproducesimagesofverypoorqualitybecauseofthenon-uniformspreadofinkandisthereforenotexercisedinonlineAFIS.Foronlinefingerprintimageacquisition,capacitativeoropticalfingerprintscannerssuchasURU4000,etc.areutilizedwhichmakeuseoftechniquessuchasfrustratedtotalinternalreflection（FTIR）[2],ultrasoundtotalinternalreflection[3],sensingofdifferentialcapacitance[4]andnoncontact3Dscanning[5]forimagedevelopment.Livescanscannersoffermuchgreaterimagequality,usuallyaresolutionof512dpi,whichresultsinsuperiorreliabilityduringmatchingincomparisontoinkedfingerprints.

Segmentationreferstotheseparationoffingerprintarea（foreground）fromtheimagebackground[6].Segmentationisusefultoavoidextractionoffeaturesinthenoisyareasoffingerprintsorthebackground.ASimplethresholdingtechnique[7]provestobeineffectivebecauseofthestreakednatureofthefingerprintarea.Thepresenceofnoiseinafingerprintimagerequiresmorevigoroustechniquesforeffectivefingerprintsegmentation.Agoodsegmentationmethodshouldexhibitthefollowingcharacteristics[8]:

·Itshouldbeinsensitivetoimagecontrast

·Itshoulddetectsmudgedornoisyregions

·Segmentationresultsshouldbeindependentofwhethertheinputimageisanenhancedimage

orarawimage

·Thesegmentationresultsshouldbeindependentofimagequality

Renetal.[8]proposedanalgorithmforsegmentationthatemploysfeaturedots,whicharethenusedtoobtainaclosesegmentationcurve.Theauthorsclaimthattheirmethodsurpassesdirectionalfieldandorientationbasedmethods[9,10,11]forfingerprintimagesegmentation.Shenetal.[12]proposedaGaborfilterbasedmethodinwhicheightGaborfiltersareconvolvedwitheachimageblockandthevarianceofthefilterresponseisusedbothforfingerprintsegmentationandqualityspecification.Xianetal.[13]proposedasegmentationalgorithmthatexploitsablock’sclusterdegree,meanandvariance.Anoptimallinearclassifierisusedforclassificationwithmorphologicalpost-processingtoremoveclassificationerrors.Bazenetal.[14]proposedapixelwisetechniqueforsegmentationinvolvingalinearcombinationofthreefeaturevectors（i.e.gradientcoherence,intensitymeanandvariance）.Afinalmorphologicalpost-processingstepisperformedtoeliminateholesinboththeforegroundandbackground.Inspiteofitshighaccuracythisalgorithmhasaveryhighcomputationalcomplexity,whichmakesitimpracticalforrealtimeprocessing.Kleinetal.[15]proposedanalgorithmthatemploysHMMstoremovetheproblemoffragmentedsegmentationencounteredduringtheuseofdifferentsegmentationalgorithms.

Foragoodqualityfingerprintfeatureextractionismucheasier,efficientandreliableincomparisontoarelativelylowerqualityfingerprint.Thequalityoffingerprintsisdegradedbyskinconditions（e.g.wetordry,cutsandbruises）,sensornoise,non-uniformcontactwithsensorsurface,andinherentlylowqualityfingerprintimages（e.g.thoseofelderlypeople,laborers）.Asignificantpercentageoffingerprintsareofpoorquality,whichmustbeenhancedfortherecognitionprocesstobeeffective.Therearetwomajorobjectivesoffingerprintenhancementi.e.i）toincreasethecontrastbetweenridgesandvalleysandii）toconnectbrokenridges.Theseobjectivescanbefulfilledbyusingacontextualfilterwhosecharacteristicsvaryaccordingtothelocalcontexttobeusedforfingerprintenhancementinsteadofconventionalimagefilters.Thefiltershouldpossesthefollowingcharacteristics:

·Itshouldprovidealowpass（averaging）effectalong

theridgedirectionwiththeaimoflinkingsmallgapsandfillingimpuritiesduetoporesornoise.

·Itshouldhaveabandpass（differentiating）effectinthedirectionorthogonaltotheridgesinordertoincreasethediscriminationbetweenridgesandvalleysandtopseparateparallellinkedridges.

Sherlocketal.[16]proposedanalgorithmforfingerprintimageenhancementthatemploysposition-dependentFourier-domain-filtering-basedorientationsmoothingandthresholdingtechnique.Greenbergetal.[17]proposedtwoschemesforfingerprintenhancement.Onemethoduseslocalhistogramequalization,Wienerfiltering,andimagebinarizationwhereastheothermethodusesauniqueanisotropicfilterfordirectgrayscaleenhancement.O’Gormanetal.[18,19]proposedacontextualfilterbasedapproachthatutilizesfourmainparametersoffingerprintimagesatagivenresolutioni.e.maximaandminimaoftheridgeandvalleywidthstoformamotherfilterwhoserotatedversionsarethenconvolvedwiththeimagetoyieldtheenhancedoutput.Hongetal.[20]proposedaneffectivemethodbasedonGaborfiltersforimageenhancement.Gaborfiltersfulfilltherequirementsofagoodfingerprintenhancementfiltermentionedearlierastheypossessboththedifferentiatingandaveragingeffects.SlightmodificationstothistechniqueweremadebyGreenbergetal.[21].Jiangweietal.[22]modifiedthetechniquegivenby[20].Theirapproachmodelsthesinusoidalshapeoftheridge-valleystructureinaclosermannerandgivesbetterresults.Ticoetal.[23]madeuseofaridgedetectingtechniquebasedontheseconddirectionalderivativeoftheimagetocarryoutfingerprintenhancement.Watsonetal.[24]multipliedtheFouriertransformofeach32x32blockbyitspowerspectrumraisedtoapowerktoproduceanefficienttechniquethatcanfinditsuseinonlinefingerprintrecognitionsystems.Fingerprintspossesstwomajortypesoffeatures:

specialtypeofpatternformedbytheridgeandfurrowstructuresinthecentralregionofthefingerprintsandminutiaedetailsassociatedwithlocalridgesandfurrows.Minutiaeareminutedetailssuchasridgeendings（apointwherearidgeendsabruptly）oraridgebifurcation（wherearidgebreaksupintotworidges）.Minutiaearecharacterizedbytheposition,directionandtype（Ridgeendingorbifurcation）.Theglobalfeaturesareusedforfingerprintclassificationintosixmajorclasseswhereastheminutiaedetailsareusedforfingerprintbasedpersonidentification.FingerprintanalystsutilizetheminutiaeinformationforfingerprintidentificationanditisthemostestablishedtechniqueinthefieldofAFIS.Theaccuracyofthetechniqueisdependentupontheprecisionintheextractionofminutiae.Therearetwomajortypeofmethodsthatareusedforminutiaeextractioni）binarizationbasedmethodsandii）directgrayscalemethods.Inbinarization-basedmethodssomeinformationislostduringbinarizationthatcandegradetheperformanceoftheminutiaeextractor.Directgrayscalemethodsovercometheseproblemsbutmaybedifficulttoimplementandtimeconsumingtooperate.Thetypicalapproachforabinarization-basedmethodinvolvesapriorienhancement,binarizationandthenthinning.Variousbinarizationandthinningapproachesarediscussedin[25,26,27,28,29].Onceabinaryskeletonhasbeenobtained,asimpleimagescanallowsthepixelscorrespondingtotheminutiaetobedetectedbyfindingthecrossingnumber.Theminutiaeobtainedasaresultofminutiaeextractionneedtobefilteredinordertoremovethefalseminutiaeintroduced.Variousminutiaefilteringtechniquesareproposedin[30,31].

Duringenrollmenttheminutiaesetobtainedfromanindividual’sfingerprintisstoredasatemplateforthatsubject.Intheauthenticationmodule,thefinger