文献翻译指纹识别系统Word文档下载推荐.docx

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histogramequalization;

ridgethinning;

ridgeending;

ridgebifurcation.

1.INTRODUCTION

Fingerprintrecognitionsystemsaretermedundertheumbrellaofbiometrics.Biometricrecognitionreferstothedistinctivephysiological（e.g.fingerprint,face,iris,retina）andbehavioral

（e.g.signature,gait）characteristics,calledbiometricidentifiersorsimplybiometrics,forautomaticallyrecognizingindividuals.In1893,itwasdiscoveredthatnotwoindividualshavesamefingerprints.Afterthisdiscoveryfingerprintswereusedincriminalidentificationandtillnowfingerprintsareextensivelyusedinvariousidentificationapplicationsinvariousfieldsoflife.Fingerprintsaregraphicalflow-likeridgespresentonhumanfingers.Theyarefullyformedataboutseventhmonthoffetusdevelopmentandfingerprintconfigurationdonotchangethroughout

thelifeexceptduetoaccidentssuchasbruisesorcutonfingertips.

Becauseofimmutabilityanduniqueness,theuseoffingerprintsforidentificationhasalwaysbeenofgreatinteresttopatternrecognitionresearchersandlawenforcementagencies.Conventionally,fingerprintrecognitionhasbeenconductedviaeitherstatisticalorsyntacticapproaches.Instatisticalapproachafingerprint’sfeaturesareextractedandstoredinann-dimensionalfeaturevectoranddecisionmakingprocessisdeterminedbysomesimilaritymeasures.Insyntacticapproach,apatternisrepresentedasastring,tree[1],orgraph[2]offingerprintfeaturesorpatternprimitivesandtheirrelations.Thedecisionmakingprocessisthensimplyasyntaxanalysisorparsingprocess.

Thispapersuggeststhestatisticalapproach.Experimentalresultsprovetheeffectivenessofthismethodonacomputerplatform,hencemakingitsuitableforsecurityapplicationswitharelativelysmalldatabase.Thepreprocessingoffingerprintsiscarriedoutusingmodifiedbasicfilteringmethodswhicharesubstantiallygoodenoughforthepurposeofourapplicationswithreasonablecomputationaltime.BlockdiagramforthecompleteprocessisshowninFigure.1.

2.IMAGEPREPROCESSING

Fortheproperandtrueextractionofminutiae,imagequalityisimprovedandimagepreprocessingisnecessaryforthefeaturesextractionbecausewecannotextracttherequiredpointsfromtheoriginalimage.Firstofall,anysortofnoisepresentintheimageisremoved.Orderstatisticsfiltersareusedtoremovethetypeofnoisewhichoccursnormallyatimageacquisition.Afterwardsthefollowingimagepreprocessingtechniquesareappliedtoenhancethefingerprintimagesformatching.

2.1HistogramEqualization

Thismethodisusedwheretheunwantedpartoftheimageismadelighterinintensitysoasto

emphasizethedesiredthedesiredpart.Figure2（a）showstheoriginalimageandFigure2（b）histogramequalizationinwhichthediscontinuitiesinthesmallareasareremoved.Forthehistogramequalization,lettheinputandtheoutputlevelforanarbitrarypixelbeiandl,respectively.Thentheaccumulationofhistogramfrom0toi（0≤i≤255,0≤k≤255）isgivenby

whereH（k）isthenumberofpixelwithgraylevelk,i.e.histogramofanarea,andC（i）isalso

knownascumulativefrequency.

2.2DynamicThresholding

Basicpurposeofthresholdingistoextracttherequiredobjectformthebackground.Thresholdingissimplythemappingofalldatapointshavinggraylevelmorethataveragegraylevel.TheresultsofthresholdingareshowninFigure3.

2.3RidgelineThinning

Beforethefeaturescanbeextracted,thefingerprintshavetobethinnedorskeletonisedsothatallridgesareonepixelthick.Whenapixelisdecidedasaboundarypixel,itisdeleteddirectlyformtheimage[3-5]orflaggedandnotdeleteduntiltheentireimagebeenscanned[6-7].Therearedeficienciesinbothcases.Intheformer,deletionofeachboundarypixelwillchangetheobjectintheimageandhenceaffecttheobjectsymmetrically.Toovercomethisproblem,somethinningalgorithmsuseseveralpassesinonethinningiteration.Eachpassisanoperationtoremoveboundarypixelsfromagivendirection.Pavlidis[8]andFieginandBen-Yosef[9]havedevelopedeffectivealgorithmsusingthismethod.However,boththetimecomplexityandmemoryrequirementwillincrease.Inthelatter,asthepixelsareonlyflagged,thestateofthebitmapattheendofthelastiterationwillusedwhendecidingwhichpixeltodelete.However,ifthisflagmapisnotusedtodecidewhetheracurrentpixelistobedeleted,theinformationgeneratedfromprocessingthepreviouspixelsincurrentiterationwillbelost.Incertainsituationsthefinalskeletonmaybebadlydistorted.Forexample,alinewithtwopixelsmaybecompletelydeleted.Recently,Zhou,QuekandNg[10]haveproposedanalgorithmthatsolvestheproblemdescribedearlierandisfoundtoperformsatisfactorilywhileprovidingareasonablecomputationaltime.ThethinningeffectisillustratedinFigure4

3.FEATURESEXTRACTION

Thetwobasicfeaturesextractedfromtheimageareridgeendingsandridgebifurcation.For

fingerprintimagesusedinautomatedidentification,ridgeendingsandbifurcationarereferredtoasminutiae.Todeterminethelocationofthesefeaturesinthefingerprintimage,a3x3windowmaskisused（Figure5）.MisthedetectedpointandX1…X8areitsneighboringpointsinaclockwisedirection.IfXnisablackpixel,thenitsresponseR（n）willbe1orotherwiseitwillbe0.IfMisanending,theresponseofthematrixwillbe

whereR（9）=R

（1）.ForMtobeabifurcation,

forexample,ifabifurcationisencounteredduringextraction,maskwillcontainthepixel

informationsuchasR

（1）=R（3）=R（4）=R（6）=R（7）=0,R

（2）=R（5）=R（8）=R（9）=1,and

Foralltheminutiaedetectedintheinterpolatedthinnedimage,thecoordinatesandtheirminutiaetypeissaveasfeaturefile.Attheendoffeatureextraction,afeaturerecordofthefingerprintisformed.

4.MATCHING

Fingerprintmatchingisthecentralpartofthispaper.Theproposedtechniqueisbasedonstructuralmodeloffingerprints[11].Oneofthemajorbreakthroughsofthismethodisitsabilitytomachfingerprintsthatareshifted,rotatedandstretched.Thisisachievedbyadifferentmatchingapproach.Asitisclearthatthisalgorithmmatchesthetwofingerprintimagescapturedatdifferenttime.Thismatchingisbasedontheminutiaeidentificationandminutiaetypematching.Matchingprocedureiscomplexduetotwomainreasons;

1）Theminutiaeofthefingerprintcapturedmayhavedifferentcoordinates

2）Theshapeofthefingerprintcapturedatdifferenttimemaybedifferentduetostretching.

Anautomatedfingerprintidentificationsystemthatisrobustmusthavefollowingcriteria:

1）Sizeoffeaturesfilemustbesmall

2）Algorithmmustbefastandrobust

3）Algorithmmustberotationallyinvariant

4）Algorithmmustberelativelystretchinvariant

Toachievethesecriteria,thestructuralmatchingmethoddescribedbyHrechakandMcHugh[11]isadoptedasthebasisofourrecognitionalgorithm,withchangesmadetothealgorithm,toprovidemorereliableandimprovingoverallmatchingspeed.Thismatchingrepresentsthelocalidentificationapproach,inwhichlocalidentifiedfeatures,theirtypeandorientationissavedinfeaturesfile,iscorrelatedwiththeotherimage’sextractedfeaturesfile.ThemodelisshowninFigure6.

Foreachextractedfeaturesonthefingerprint,aneighborhoodofsomespecifiedradiusRaboutthecentralfeatureisdefinedandthenEuclideandistanceandrelativeanglesbetweenthecentralpointandtheotherpointisnotedwiththepoint’stype.Sincethedistanceamongthepoint

remainsthesamethroughoutthelife.Sothistechniqueworkswellfortherotatedandshiftedimages.

5.CONCLUSION

Afingerprintrecognitionalgorithmthatisfast,accurateandreliablehasbeensuccessfullyimplemented.Thisalgorithmcanbemodified,introducingtheridgelinecount,andthencouldbe

usedinonlineandrealtimeautomatedidentificationandrecognitionsystem.

REFERENCES

[1]MOAYER,B.,andFU,K.S.:

‘Atreesystemapproachforfingerprintpatternrecognition’,IEEETrans.,1986,PAMT-8,（3）,pp.376-387

[2]ISENOR,D.K.,andZAKY,S.G.:

‘Fingerprintidentificationusinggraphmatching’,PatternRecognit.,1986,19,

（2）pp.113-122

[3]TAMURA,H.:

‘Acomparisonoflinethinningalgorithmsfromdigitalgeometryviewpoint’.ProceedingsoffourthinternationaljointconferenceonPatternRecognition,Kyoto,Nov.1978,pp.715-719

[4]HILDITCH,C.J.:

’Linearskeletonfromsquarecupboards’,MachineIntel.,1969,4,pp.403-420

[5]NACCACHE,N.J.,andSHINCHAL,R.:

‘AninvestigationintotheskeletonizationapproachofHilditch’,

PatternRecognit.,1984,17,（3）,pp.279-284

[6]JANG,B.K.,andCHIN,P.T.:

‘Analysisofthinninga