文献翻译——基于细节分数匹配法的指纹识别

第1页外文文献资料FINGERPRINTRECOGNITIONUSINGMINUTIASCOREMATCHINGABSTRACT:ThepopularBiometricusedtoauthenticateapersonisFingerprintwhichisuniqueandpermanentthroughoutapersonslife.Aminutiamatchingiswidelyusedforfingerprintrecognitionandcanbeclassifiedasridgeendingandridgebifurcation.InthispaperweprojectedFingerprintRecognitionusingMinutiaScoreMatchingmethod(FRMSM).ForFingerprintthinning,theBlockFilterisused,whichscanstheimageattheboundarytopreservesthequalityoftheimageandextracttheminutiaefromthethinnedimage.Thefalsematchingratioisbettercomparedtotheexistingalgorithm.Key-words:-FingerprintRecognition,Binarization,BlockFilterMethod,MatchingscoreandMinutia.1.IntroductionBiometricsystemsoperateonbehavioralandphysiologicalbiometricdatatoidentifyaperson.Thebehavioralbiometricparametersaresignature,gait,speechandkeystroke,theseparameterschangewithageandenvironment.Howeverphysiologicalcharacteristicssuchasface,fingerprint,palmprintandirisremainsunchangedthroughoutthelifetimeofaperson.Thebiometricsystemoperatesasverificationmodeoridentificationmodedependingontherequirementofanapplication.Theverificationmodevalidatesapersonsidentitybycomparingcapturedbiometricdatawithreadymadetemplate.Theidentificationmoderecognizesapersonsidentitybyperformingmatchesagainstmultiplefingerprintbiometrictemplates.Fingerprintsarewidelyusedindailylifeformorethan100yearsduetoitsfeasibility,distinctiveness,permanence,accuracy,reliability,andacceptability.Fingerprintisapatternofridges,furrowsandminutiae,whichareextractedusinginkedimpressiononapaperorsensors.Agoodqualityfingerprintcontains25to80minutiaedependingonsensorresolution第2页andfingerplacementonthesensor.Thefalseminutiaearethefalseridgebreaksduetoinsufficientamountofinkandcross-connectionsduetooverinking.Itisdifficulttoextractreliablyminutiafrompoorqualityfingerprintimpressionsarisingfromverydryfingersandfingersmutilatedbyscars,scratchesduetoaccidents,injuries.MinutiabasedfingerprintrecognitionconsistsofThinning,Minutiaeextraction,MinutiaematchingandComputingmatchingscore.Motivation:Themotivationbehindtheworkisgrowingneedtoidentifyapersonforsecurity.Thefingerprintisoneofthepopularbiometricmethodsusedtoauthenticatehumanbeing.TheproposedfingerprintverificationFRMSMprovidesreliableandbetterperformancethantheexistingtechnique.Contribution:InthispaperweusedFingerprintRecognitionusingMinutiaScoreMatchingmethodwiththehelpofMATLABcodes.Minutiaeareextractedfromthethinnedimageforbothtemplateandinputimage.Finallyboththeimagesaresubjectedtomatchingprocessandmatchingscoreiscomputed.Organization:Thispaperisorganizedintothefollowingsections.SectionIIisandefinitionoftherelatedworkanddescribesModelforfingerprintrecognitionindetail,SectionIIIgivesthealgorithm.InsectionIVperformanceanalysisandresultsarediscussedandfinallyinsectionVgivetheconclusions2.RelatedworkG.SambasivaRaoetal.,proposedfingerprintidentificationtechniqueusingagraylevelwatershedmethodtofindouttheridgespresentonafingerprintimagebydirectlyscannedfingerprintsorinked36impression.RobertHastingsdevelopedamethodforenhancingtheridgepatternbyusingaprocessoforienteddiffusionbyadaptationofanisotropicdiffusiontosmooththeimageinthedirectionparalleltotheridgeflow.Theimageintensityvariessmoothlyasonetraversealongtheridgesorvalleysbyremovingmostofthesmallirregularitiesandbreaksbutwiththeidentityoftheindividualridgesandvalleyspreserved.JinweiGu,etal.,proposedamethodfor第3页fingerprintverificationwhichincludesbothminutiaeandmodelbasedorientationfieldisused.Itgivesrobustdiscriminatoryinformationotherthanminutiaepoints.Fingerprintmatchingisdonebycombiningthedecisionsofthematchersbasedontheorientationfieldandminutiae.V.VijayaKumariandN.SuriyanarayananproposedamethodforperformancemeasureoflocaloperatorsinfingerprintbydetectingtheedgesoffingerprintimagesusingfivelocaloperatorsnamelySobel,Roberts,Prewitt,CannyandLoG.Theedgedetectedimageisfurthersegmentedtoextractindividualsegmentsfromtheimage.RajuSonavane,andB.S.Sawantpresentedamethodbyintroducingaspecialdomainfingerprintenhancementmethodwhichdecomposesthefingerprintimageintoasetoffilteredimagesthenorientationfieldisestimated.Aqualitymaskdistinguishestherecoverableandunrecoverablecorruptedregionsintheinputimagearegenerated.Usingtheestimatedorientationfield,theinputfingerprintimageisadaptivelyenhancedintherecoverableregions.EricP.Kukula,etal.,purposedamethodtoinvestigatetheeffectoffivedifferentforcelevelsonfingerprintmatchingperformance,imagequalityscores,andminutiaecountbetweenopticalandcapacitancefingerprintsensors.Threeimageswerecollectedfromtherightindexfingersof75participantsforeachsensingtechnology.Descriptivestatistics,analysisofvariance,andKruskal-Wallisnonparametrictestswereconductedtoassesssignificantdifferencesinminutiaecountsandimagequalityscoresbasedontheforcelevel.Theresultsrevealasignificantdifferenceinimagequalityscorebasedontheforcelevelandeachsensortechnology,yetthereisnosignificantdifferenceinminutiaecountbasedontheforcelevelsofthecapacitancesensor.Theimagequalityscore,showntobeeffectedbyforceandsensortype,isoneofmanyfactorsthatinfluencethesystemmatchingperformance,yettheremovaloflowqualityimagesdoesnotimprovethesystemperformanceateachforcelevel.M.R.Girgisaetal.,proposedamethodtodescribeafingerprintmatchingbasedonlinesextractionandgraphmatchingprinciplesbyadoptingahybridschemewhichconsistsofageneticalgorithmphaseandalocalsearchphase.Experimentalresultsdemonstrate第4页therobustnessofalgorithm.LupingJi,andZhangYiproposedamethodforestimatingfourdirectionorientationfieldbyconsideringfoursteps,i)preprocessingfingerprintimage,ii)determiningtheprimaryridgeoffingerprintblockusingneuronpulsecoupledneuralnetwork,iii)estimatingblockdirectionbyprojectivedistancevarianceofaridge,insteadofafullblock,iv)correctingtheestimatedorientationfield.DuoqianMaioetal.,usedprincipalgraphalgorithmbykegltoobtainprincipalcurvesforautofingerprintidentificationsystem.Fromprincipalcurves,minutiaeextractionalgorithmisusedtoextracttheminutiaeofthefingerprint.Theexperimentalresultsshowscurvesobtainedfromgraphalgorithmaresmootherthanthethinningalgorithm.AlessandraLumini,andLorisNannidevelopedamethodforminutiaebasedfingerprintanditsapproachtotheproblemastwo-classpatternrecognition.TheobtainedfeaturevectorbyminutiaematchingisclassifiedintogenuineorimposterbySupportVectorMachineresultingremarkableperformanceimprovementXifengTongetal.,proposedamethodtoovercomenonlineardistortionusingLocalRelativeErrorDescriptor(LRLED).Thealgorithmconsistsofthreestepsi)apairwisealignmentmethodtoachievefingerprintalignmentii)amatchedminutiaepairsetisobtainedwithathresholdtoreducenon-matchesfinallyiii)theLRLEDbasedsimilaritymeasure.LRLEDisgoodatdistinguishingbetweencorrespondingandnoncorrespondingminutiae-pairsandworkswellforfingerprintminutiaematching.L.Lametal.,presentedamethod,thinningistheprocessofreducingthicknessofeachlineofpatternstojustasinglepixelwidth.Therequirementsofagoodalgorithmwithrespecttoafingerprintarei)thethinnedfingerprintimageobtainedshouldbeofsinglepixelwidthwithnodiscontinuitiesii)Eachridgeshouldbethinnedtoitscentralpixeliii)Noiseandsingularpixelsshouldbeeliminatediv)nofurtherremovalofpixelsshouldbepossibleaftercompletionofthinningprocess.Mohamedetal.,presentedfingerprintclassificationsystemusingFuzzyNeuralNetwork.Thefingerprintfeaturessuchassingularpoints,positionsanddirectionofcoreanddeltaobtainedfromabinarisedfingerprintimage.Themethodisproducinggoodclassificationresults.Ching-TangHsiehandChia-ShingHu14hasdevelopedanoidmethodforFingerprintrecognition.Ridge第5页bifurcationsareusedasminutiaeandridgebifurcationalgorithmwithexcludingthenoiselikepointsareproposed.Experimentalresultsshowthehumanoidfingerprintrecognitionisrobust,reliableandrapid.LieWeiproposedamethodforrapidsingularitiessearchingalgorithmwhichusesdeltafieldPoincareindexandarapidclassificationalgorithmtoclassifythefingerprintinto5classes.Thedetectionalgorithmsearchesthedirectionfieldwhichhasthelargerdirectionchangestogetthesingularities.Singularitiesdetectionisusedtoincreasetheaccuracy.HartwigFronthaler,etal.,Proposedfingerprintenhancementtoimprovethematchingperformanceandcomputationalefficiencybyusinganimagescalepyramidanddirectionalfilteringinthespatialdomain.ManaTarjomanandShaghayeghZareiintroducedstructuralapproachtofingerprintclassificationsbyusingthedirectionalimageoffingerprintinsteadofRavi.J.etal/InternationalJournalofEngineeringScienceandTechnologysingularities.Directionalimageincludesdominantdirectionofridgelines.BhupeshGouretalhavedevelopedamethodforextractionofminutiaefromfingerprintimagesusingmidpointridgecontourrepresentation.Thefirststepissegmentationtoseparateforegroundfrombackgroundoffingerprintimage.A64x64regionisextractedfromfingerprintimage.Thegrayscaleintensitiesin64x64regionsarenormalizedtoaconstantmeanandvariancetoremovetheeffectsofsensornoiseandgrayscalevariationsduetofingerpressuredifferences.Afterthenormalizationthecontrastoftheridgesareenhancedbyfiltering64x64normalizedwindowsbyappropriatelytunedGaborfilter.Processedfingerprintimageisthenscannedfromtoptobottomandlefttorightandtransitionsfromwhite(background)toblack(foreground)aredetected.Thelengthvectoriscalculatedinalltheeightdirectionsofcontour.Eachcontourelementrepresentsapixelonthecontour,containsfieldsforthex,ycoordinatesofthepixel.Theproposedmethodtakeslessanddonotdetectanyfalseminutiae.SharathPankantietal.,proposedScaleInvariantFeatureTransformation(SIFT)torepresentandmatchthefingerprint.ByextractingcharacteristicSIFTfeaturepointsinscalespaceandperformmatchingbasedonthetextureinformationaroundthefeaturepoints.ThecombinationofSIFTandconventionalminutiaebasedsystem第6页achievessignificantlybetterperformancethaneitheroftheindividualschemes.ManvjeetKauretal.,haveintroducedcombinedmethodstobuildaminutiaextractorandaminutiamatcher.SegmentationwithMorphologicaloperationsusedtoimprovethinning,falseminutiaeremoval,minutiamarking.HaipingLposedaneffectiveandefficientalgorithmforminutiaeextractiontoimprovetheoverallperformanceofanautomaticfingerprintidentificationsystembecauseitisveryimportanttopreservetrueminutiaewhileremovingspuriousminutiaeinpost-processing.Theproposednovelfingerprintimagepost-processingalgorithmmakesaneffortstoreliablydifferentiatespuriousminutiaefromtrueonesbymakinguseofridgenumberinformation,referringtooriginalgray-levelimage,designingandarrangingvariousprocessingtechniquesproperly,andalsoselectingvariousprocessingparameterscarefully.Theproposedpost-processingalgorithmiseffectiveandefficient.PrabhakarS,Jain.A.K.etal.hasdevelopedfilter-basedrepresentationtechniqueforfingerprintidentification.Thetechniqueexploitsbothlocalandglobalcharacteristicsinafingerprinttomakeidentification.Eachfingerprintimageisfilteredinanumberofdirectionsanda640-dimensinalfeaturevectorisextractedinthecentralregionofthefingerprint.Thefeaturevectoriscompactandrequiresonly640bytes.ThematchingstagecomputestheEuclidiandistancebetweenthetemplatefingercodeandtheinputfingercode.Themethodgivesgoodmatchingwithhighaccuracy.BallanMintroducedDirectionalFingerprintProcessingusingfingerprintsmoothing,classificationandidentificationbasedonthesingularpoints(deltaandcorepoints)obtainedfromthedirectionalhistogramsofafingerprint.FingerprintsareclassifiedintotwomaincategoriesthatarecalledLassoandWirbel.Theprocessincludesdirectionalimageformation,directionalimageblockrepresentation,singularpointdetectionanddecision.Themethodgivesmatchingdecisionvectorswithminimumerrors,andmethodissimpleandfast.3.MODELInthissectionthedefinitionsandFRMSMmodelarediscussed第7页A.Definitions:Termination:Thelocationwherearidgecomestoanend.Bifurcation:Thelocationwherearidgedividesintotwoseparateridges.Binarization:Theprocessofconvertingtheoriginalgrayscaleimagetoablack-andwhiteimage.Thinning:TheprocessofreducingthewidthofeachridgetoonepixelTerminationAngle:Theanglebetweenthehorizontalandthedirectionoftheridge.BifurcationAngle:TheanglebetweenthehorizontalandthedirectionofthevalleyendingbetweenthebifurcationsFalseMatchingRatio:Itistheprobabilitythatthesystemwilldecidetoallowaccesstoan(FMR)imposterisgiveninanequation(1).(1)mptsosterAFalMchRITheimposterattemptsareimplementedbymatchingeachinputimagewithallthetemplateimages.Falsematchwasrecordedforeachimposterattemptwhenthematchingscorewasgreaterthantheestablishedthreshold.(viii)FalseNonMatchingRatio(FNMR):Itistheprobabilitythatthesystemdeniesaccesstoanapproveduserisgiveninanequation(2).(2)mptsEnrolAechFasNMRWhere,NTandNIrepresentthetotalnumberofminutiaeinthetemplateandinputmatricesrespectively.Bythisdefinition,thematchingscoretakesonavaluebetween0and1.Matchingscoreof1and0indicatesthatdatamatchesperfectlyanddataiscompletelymismatchedrespectively.B.ModelFigure1givestheblockdiagramofFRMSMwhichisusedtomatchthetestfingerprintwiththetemplatedatabaseusingMinutiaMatchingScore.FingerprintImage:Theinputfingerprintimageisthegrayscaleimageofaperson,whichhasintensityvaluesrangingfrom0to255.Inafingerprintimage,theridgesappearasdarklineswhilethevalleysarethelightareasbetweentheridges.Minutiaepointsarethelocationswherearidgebecomes第8页discontinuous.Aridgecaneithercometoanend,whichiscalledasterminationoritcansplitintotworidges,whichiscalledasbifurcation.Thetwominutiaetypesofterminationsandbifurcationsareofmoreinterestforfurtherprocessescomparedtootherfeaturesofafingerprintimage.Fig1:BlockDiagramofFRMSM.Binarization:Thepre-processingofFRMSMusesBinarizationtoconvertgrayscaleimageintobinaryimagebyfixingthethresholdvalue.Thepixelvaluesaboveandbelowthethresholdaresetto1and0respectively.AnoriginalimageandtheimageafterBinarizationareshownintheFigure2.(a)(b)Fig2:(a)OriginalFingerprint(b)Binarizedimage.第9页BlockFilter:ThebinarizedimageisthinnedusingBlockFiltertoreducethethicknessofallridgelinestoasinglepixelwidthtoextractminutiaepointseffectively.Thinningdoesnotchangethelocationandorientationofminutiaepointscomparedtooriginalfingerprintwhichensuresaccurateestimationofminutiaepoints.Thinningpreservesoutermostpixelsbyplacingwhitepixelsattheboundaryoftheimage,asaresultfirstfiveandlastfiverows,firstfiveandlastfivecolumnsareassignedvalueofone.Dilationanderosionareusedtothintheridges.AbinarizedFingerprintandtheimageafterthinningareshowninFigure3.(a)(b)Fig3:(a)BinarizedFingerprint(b)Imageafterthinning4.AlgorithmProblemdefinition:GiventhetestFingerprintImagetheobjectivesare,1.Pre-processingthetestFingerprint.2.Extracttheminutiaepoints.3.MatchingtestFingerprintwiththedatabase.Table1givesthealgorithmforfingerprintverification,inwhichinputtestfingerprintimageiscomparedwithtemplatefingerprintimage,forrecognition.Table1:AlgorithmofFRMSMInput:Gray-scaleFingerprintimage.Output:Verifiedfingerprintimagewithmatchingscore.1.Fingerprintisbinarized2.Thinningonbinarizedimage第10页3.Minutiaepointsareextracted.Datamatrixisgeneratedtogettheposition,orientationandtypeofminutiae.4.Matchingoftestfingerprintwithtemplate5.Matchingscoreoftwoimagesiscomputed,ifmatchingscoreis1imagesarematchedandifitis0thentheyaremismatched.5.PerformanceAnalysisandResultsForperformanceanalysis,weconsideredlargefingerprintdatabaseimageshavingdifferentpatternssuchasfingerprintleftloop,rightloop,whorlandarchasshownintheFigure4.Fig4:SamplesoffingerprintimagesTable2givesthecomparisonofFalseNonMatchingRatio(FNMR)andFalseMatchingRatios(FMR)forexistingmethodofFingerprintRecognitionFuzzyNeuralNetwork(FRFNN)andproposedmethodofFingerprintRecognitionusingMinutiaScoreMatchingmethod(FRMSM).ItisobservedthattheFalseNonMatchingRatioforboththemethodsiszeroandFalseMatchingRatioforexistingmethodis0.23whereasfortheproposedmethodFRMSMis0.026.Table2:ComparisonofFNMRandFMRforFRFNNandFRMSM.FRFNNFRMSMFNMR0.000.00FMR0.230.026第11页6.ConclusionInthispaper,wepresentedFingerprintmatchingusingFRMSM.Thepre-processingtheoriginalfingerprintinvolvesimagebinarization,ridgethinning,andnoiseremoval.FingerprintRecognitionusingMinutiaScoreMatchingmethodisusedformatchingtheminutiapoints.TheproposedmethodFRMSMgivesbetterFMRvaluescomparedtotheexistingmethod.第0页中文翻译稿基于细节分数匹配法的指纹识别摘要：:验证生物特征技术用于识别一个人在生活中独特的和永久不变的指纹是当下很流行的。细节匹配可分为岭匹配和脊分叉，广泛用于指纹识别技术中。在本文中,我们使用细节分数匹配方法(FRMSM)来进行指纹识别。指纹细化,使用滤波器扫描图像的块,保持边界图像质量,从而减少图像的提取细节。错误的匹配率算法比现有的算法好。关键词:指纹识别、二值化、块过滤方法,匹配分数和细节。1.介绍生物识别系统操作行为和生理生物特征数据来识别一个人。行为生物参数签名、步态、演讲和击键,这些参数与年龄和环境变化。然而生理特征如脸、指纹、手掌印和虹膜不变通过一个人的一生。生物识别系统是验证模式或识别模式取决于应用程序的需求。验证一个人的身份验证模式,比较了生物识别数据和现成的模板。识别模式识别一个人的身份通过执行匹配多个指纹生物特征模板。指纹广泛应用于日常生活100多年来由于其可行性,独特性,永久,准确性、可靠性和可接受性。指纹是一个模式的山脊,沟和细节,这是给纸或传感器提取使用签署的印象。质量好的指纹包含25-80细节取决于传感器分辨率和手指位置传感器。假细节是假岭休息不足数量的墨水和交叉连接由于署名。很难从低质量指纹提取可靠的琐事引起的印象非常干燥的手指和手指残缺不全的伤疤,划痕由于事故、受伤。细节基于指纹识别由稀疏、提取细节,细节匹配和计算匹配分数。动机:动机工作越来越需要识别一个人的安全。指纹是一种流行的生物识别方法用于验证人类。该指纹验证FRMSM提供可靠和更好的性能比现有的技术。贡献:在本文中,我们使用指纹识别使用细节分数匹配方法借助MATLAB代码。细节都从减少图像中提取模板和输入的形象。最后的图像进行匹配和匹配分数计算过程。组织:本文以下部分被组织成,第二部分给出了相关工作，第三部分给出了定义和模型，第四部分给出算法，第五节中对性能和结果进行了分析和讨论,最后在第五部分给出结论2.相关工作g.SambasivaRaoetal.,提出了指纹识别技术使用灰度分水岭找出方法对指纹图像第1页的脊礼物直接扫描指纹或签署的印象。罗伯特黑斯廷斯开发了一种方法来提高岭模式使用的过程适应面向扩散的各向异性扩散平滑图像的方向平行山脊流动。作为一个遍历图像强度变化平稳去除大部分的山脊和山谷小的违规行为,但个人的身份山脊和山谷保留。提出的方法基于指纹验证包括细节和模型使用定位领域。它给健壮的歧视性的细节点以外的信息。Vijaya库玛丽和n.Suriyanarayanan提出了一种方法来衡量工作表现的地方运营商在指纹检测指纹图像的边缘使用5名当地运营商即索贝尔,罗伯茨,普瑞维特,精明的和日志。边缘检测图像进一步分割提取个人部分从形象。工程学士学位,之后获RajuSonavaneSawant5提出了一种方法通过引入一个特殊的领域指纹增强方法在指纹图像分解为一组过滤图像方向场估计。一个高质量的面具区分可恢复和不可恢复的损坏区域在输入图像生成。使用估计取向字段中,输入指纹图像自适应增强的可恢复的地区。埃里克pKukulaetal.计划的方法调查五个不同的力量水平的影响指纹匹配性能,图像质量分数,光学和电容之间和细节指纹传感器。右手中指的三张图片收集每个传感75名参与者技术。描述性统计、方差分析和Kruskal-Wallis非参数进行了测试评估重要细节的差异数和图像质量分数基于力水平。结果显示出显著差异在图像质量分数基于力水平和每个传感器技术,没有显著差异细节计算基于电容传感器的力量水平的图像质量分数,证明由力和传感器类型,是众多因素影响之一系统匹配性能,然而除去低质量图像并不能提高系统性能在每个力水平。m.r.Girgisaetal.,提出了一种基于线来描述指纹匹配方法提取和图匹配原则,采用遗传算法的混合方案由阶段和一个本地搜索阶段。实验结果证明了算法的鲁棒性。Luping霁,提出了一种估算方法四个方向取向领域通过考虑四个步骤,一)预处理指纹图像,(二)确定指纹块使用的主脊神经元脉冲耦合神经网络、三)估计块方向的投影距离方差岭,而不是一个完整的块,(四)纠正方向场估计。DuoqianMaioetal.,主要kegl图算法的使用获得主要曲线自动指纹识别系统。从主曲线,细节提取算法用于提取指纹的细节。实验结果显示曲线获得图算法比稀释平滑算法。亚历山德拉Lumini,懒猴Nanni了方法基于细节指纹及其方法两类模式识别的问题。获得特征向量的细节匹配分为真正的或由支持向量机冒名顶替者产生显著的性能改进西峰通etal提出了一种方法来克服非线性失真使用本地相对误差描述符(LRLED)。该算法由三个步骤一)一对明智的对齐方法实现指纹比对二)一对匹配的细节是获得阈值,以减少non-matches最后三)LRLED基于相似性度量。LRLED擅长区分相应的对应和非minutiae-pairs和适用于指纹细