指紋確認中英文翻譯、外文文獻翻譯、外文翻譯

FingerprintIdentificationBySalilPrabhakar,AnilJainFingerprintMatching:Amongallthebiometrictechniques,fingerprint-basedidentificationistheoldestmethodwhichhasbeensuccessfullyusedinnumerousapplications.Everyoneisknowntohaveunique,immutablefingerprints.Afingerprintismadeofaseriesofridgesandfurrowsonthesurfaceofthefinger.Theuniquenessofafingerprintcanbedeterminedbythepatternofridgesandfurrowsaswellastheminutiaepoints.Minutiaepointsarelocalridgecharacteristicsthatoccurateitheraridgebifurcationoraridgeending.Fingerprintmatchingtechniquescanbeplacedintotwocategories:minutae-basedandcorrelationbased.Minutiae-basedtechniquesfirstfindminutiaepointsandthenmaptheirrelativeplacementonthefinger.

However,therearesomedifficultieswhenusingthisapproach.Itisdifficulttoextracttheminutiaepointsaccuratelywhenthefingerprintisoflowquality.Alsothismethoddoesnottakeintoaccounttheglobalpatternofridgesandfurrows.Thecorrelation-basedmethodisabletoovercomesomeofthedifficultiesoftheminutiae-basedapproach.

However,ithassomeofitsownshortcomings.Correlation-basedtechniquesrequirethepreciselocationofaregistrationpointandareaffectedbyimagetranslationandrotation.Fingerprintmatchingbasedonminutiaehasproblemsinmatchingdifferentsized(unregistered)minutiaepatterns.Localridgestructurescannotbecompletelycharacterizedbyminutiae.Wearetryinganalternaterepresentationoffingerprintswhichwillcapturemorelocalinformationandyieldafixedlengthcodeforthefingerprint.ThematchingwillthenhopefullybecomearelativelysimpletaskofcalculatingtheEuclideandistancewillbetweenthetwocodesWearedevelopingalgorithmswhicharemorerobusttonoiseinfingerprintimagesanddeliverincreasedaccuracyinreal-time.Acommercialfingerprint-basedauthenticationsystemrequiresaverylowFalseRejectRate(FAR)foragivenFalseAcceptRate(FAR).Thisisverydifficulttoachievewithanyonetechnique.Weareinvestigatingmethodstopoolevidencefromvariousmatchingtechniquestoincreasetheoverallaccuracyofthesystem.Inarealapplication,thesensor,theacquisitionsystemandthevariationinperformanceofthesystemovertimeisverycritical.Wearealsofieldtestingoursystemonalimitednumberofuserstoevaluatethesystemperformanceoveraperiodoftime.FingerprintClassification:Largevolumesoffingerprintsarecollectedandstoredeverydayinawiderangeofapplicationsincludingforensics,accesscontrol,anddriverlicenseregistration.Anautomaticrecognitionofpeoplebasedonfingerprintsrequiresthattheinputfingerprintbematchedwithalargenumberoffingerprintsinadatabase(FBIdatabasecontainsapproximately70millionfingerprints!).Toreducethesearchtimeandcomputationalcomplexity,itisdesirabletoclassifythesefingerprintsinanaccurateandconsistentmannersothattheinputfingerprintisrequiredtobematchedonlywithasubsetofthefingerprintsinthedatabase.

Fingerprintclassificationisatechniquetoassignafingerprintintooneoftheseveralpre-specifiedtypesalreadyestablishedintheliteraturewhichcanprovideanindexingmechanism.Fingerprintclassificationcanbeviewedasacoarselevelmatchingofthefingerprints.Aninputfingerprintisfirstmatchedatacoarseleveltooneofthepre-specifiedtypesandthen,atafinerlevel,itiscomparedtothesubsetofthedatabasecontainingthattypeoffingerprintsonly.Wehavedevelopedanalgorithmtoclassifyfingerprintsintofiveclasses,namely,whorl,rightloop,leftloop,arch,andtentedarch.Thealgorithmseparatesthenumberofridgespresentinfourdirections(0degree,45degree,90degree,and135degree)byfilteringthecentralpartofafingerprintwithabankofGaborfilters.ThisinformationisquantizedtogenerateaFingerCodewhichisusedforclassification.Ourclassificationisbasedonatwo-stageclassifierwhichusesaK-nearestneighborclassifierinthefirststageandasetofneuralnetworksinthesecondstage.Theclassifieristestedon4,000imagesintheNIST-4database.Forthefive-classproblem,classificationaccuracyof90%isachieved.Forthefour-classproblem(archandtentedarchcombinedintooneclass),weareabletoachieveaclassificationaccuracyof94.8%.Byincorporatingarejectoption,theclassificationaccuracycanbeincreasedto96%forthefive-classclassificationandto97.8%forthefour-classclassificationwhen30.8%oftheimagesarerejected.FingerprintImageEnhancementAcriticalstepinautomaticfingerprintmatchingistoautomaticallyandreliablyextractminutiaefromtheinputfingerprintimages.However,theperformanceofaminutiaeextractionalgorithmreliesheavilyonthequalityoftheinputfingerprintimages.Inordertoensurethattheperformanceofanautomaticfingerprintidentificationverificationsystemwillberobustwithrespecttothequalityofthefingerprintimages,itisessentialtoincorporateafingerprintenhancementalgorithmintheminutiaeextractionmodule.Wehavedevelopedafastfingerprintenhancementalgorithm,whichcanadaptivelyimprovetheclarityofridgeandfurrowstructuresofinputfingerprintimagesbasedontheestimatedlocalridgeorientationandfrequency.Wehaveevaluatedtheperformanceoftheimageenhancementalgorithmusingthegoodnessindexoftheextractedminutiaeandtheaccuracyofanonlinefingerprintverificationsystem.Experimentalresultsshowthatincorporatingtheenhancementalgorithmsimprovesboththegoodnessindexandtheverificationaccuracy.FingerprintIdentifiesArithmeticThefingerprinttechniqueofscanscandividedinto2typesgenerally:identificationsystem,suchasAFIS(automaticfingerprintconfirmsystem)andverificationsystem,twokindsofsystemkeyofthedifferentiationisinthefingerprinttemplate.verificationthesystemequallyneedstoobtainfingerprintimage,butthiskindoftechniquedoesn'tkeepcompletefingerprintimageanditjustkeepsthroughsomeparticulardatasthatsomecalculatewayprocessingschaseafingerprintinanoppositesmallertemplate(250-1000wordbyte).Whenthesedatasarepickupafter,thefingerprintportraitwon'tbeagainkeepandcan'tscantemplatetorebuildthroughafinger,either.Forthis,manycompanieswithdomesticandinternationalinthelastyearsesanditsthinkfactoryproducedmanyarithmeticindigitalways.Evaluatingaexcellentlyarithmeticwhichcommercialandbigareaexpand,notonlyneedfromthemiscarriageofjusticerateofnormalregulations,refusedtojudgearate,oppositeaccuracy,refusedtoascendarateetc.parametertoevaluate,agoodcalarithmeticincludesvarious,forexample:Canenoughfilterinadditiontofingerprintnoise?Adaptdifferentangletopresstopress?Adaptdifferentfingerprintquality?Whetherinconsiderationofdoesthehighspeedmatch?Canfilteraremnantsremainingfingerprintinformation?Whethercanorderintheasfaraspossiblelittlecharacteristicunderidentify?Adaptafingerprintdissimilaritythevarietyoftheseason?Canhandleatoodryormoistfingerprint?Canadaptadissimilaritytopressthepressuredegree?Occupyaquitealittlememory?Whetherverylowtothedependenceofsystem?Whethercangoodmovementundervariousdifferentoperationenvironments?Canconvenientlytransplanttogotosingleslicemachinesystem?Canrunoutthequantitylittlecharacteristictoexpressafingerprintinformation?Doesthecustomerfeelverycomfortable?Whetherthedevelopmentsystemopenedverymuchtoexpandamarket?Whetherthroughagreatdealoftestoffingerprintdatabase?Canletthecustomerexperienceatransparenttest?Havelow-downbusinessthreshold?Whetherhaveexcellentof1:Nperformance?Doesthesoftwareconnectwhethermatchesinternationalnormornot?Canadapttheportraitofdifferentquality?Canprovidetoconnectforcustomergooddevelopment?Etc.Theprincipleoffingerprintidentifiesarithmeticisaftertheimagewaspickedupwhichisahighqualityandhastobeconvertedintoanusefulformatforit.Ifimageisashdegree,oppositemoreshallowpartwillbeabandon,butoppositedeeperpartbebecomeblack.Thepixelofridgeisbeenthinby5-8toarriveapixel,soabilitytheprecisionpositiontheridgebreakpointanddiverged.Suchas:Aarithmeticpossibilityatinspectionalimagepickandgetridofadetailofthetwoclosedetail,becausethesetwodetailsnearedtoomuch,becauseofthescarformation,sweatliquidordustcauseofdetailabnormality,thearithmeticisincapableforthedinttothesecircumstances.Perhaps,oneforktobelocatedonanislandformscarperpendicularcutthrough2-3ridgeson(maybeafalsedetail)perhapsaridge.(maybescarformationordust)Allthispossibledetailswanttobeabandoninthisprocessing.Onceapointisindeedsettledown,itspositionbeorigin(0,0)ofX,Yaxile,inthedetailthepointofthefixedpositionprocess,ortheplaceridgebesquareupwardoftheterminalpointhaveacorner.(thecircswhenthearchbreakuppointappearedwillbemorecomplicated)指紋確認SalilPrabhakar,AnilJain指紋匹配:在所有的生物技術(shù)之中，指紋識別技術(shù)已經(jīng)被成功地應(yīng)用于很多場合。每個人都知道指紋具有唯一不可變的特性。一個人的指紋是由手指的表面上一系列脊和溝做成的。指紋的獨特性是由脊和溝的式樣和細節(jié)點決定，細節(jié)點是發(fā)生一個脊分叉或一個脊終止時的當(dāng)?shù)氐奶卣鼽c。指紋匹配技術(shù)可以分為兩種:基于細節(jié)的和基于相互關(guān)系的。以細節(jié)為基礎(chǔ)的技術(shù)首先發(fā)現(xiàn)細節(jié)點，然后在手指上映射出他們的相對位置。然而，當(dāng)使用這種方法時還有一些困難，當(dāng)指紋質(zhì)量低的時候，正確地吸取細節(jié)點很困難。這一方法也不考慮全球的指紋的脊和溝的式樣。以相互關(guān)系為基礎(chǔ)的方法能夠克服以細節(jié)為基礎(chǔ)方法的一些困難。然而，它也有一些自己的缺點。以相互關(guān)系為基礎(chǔ)的技術(shù)需要精確定位登記點而且被圖像翻譯和旋轉(zhuǎn)所影響?；诩毠?jié)的指紋匹配在匹配不同尺寸(未注冊的)的細節(jié)式樣的指紋方面有問題，當(dāng)?shù)氐募菇Y(jié)構(gòu)不能完全地擁有細節(jié)的特點。我們正在嘗試為指紋取得更多的當(dāng)?shù)財?shù)據(jù)并且產(chǎn)生一個固定的長度密碼。這時匹配將會變成像希望的那樣計算在這二個密碼之間的歐幾里得幾何距離的相對簡單工作。我們正在發(fā)展更強健的在指紋圖像中去除噪音性強，遞送準(zhǔn)確性強，實時性強的運算法則。一個商業(yè)的指紋確認系統(tǒng)需要非常低的錯誤率(FAR)一個可以接受的錯誤比率.(FAR)這對于任何的一項技術(shù)都是一個難點。我們正在研究各種不同的相配技術(shù)來增加系統(tǒng)的準(zhǔn)確性。在一個實時的應(yīng)用環(huán)境中或在傳感器中，系統(tǒng)的執(zhí)行時間變的越來越重要。我們也是把我們的系統(tǒng)實地試驗在少數(shù)用戶上一段時間來評估我們的系統(tǒng)。指紋分類:每天大量的指紋被收集存儲應(yīng)用在不同的場合，例如法醫(yī)，通路控制和駕駛員執(zhí)照登記。以指紋為基礎(chǔ)的自動識別系統(tǒng)需要輸入一個與數(shù)據(jù)庫中相匹配的指紋(聯(lián)邦調(diào)查局?jǐn)?shù)據(jù)庫大約包含七千萬個指紋!)。為了減少搜尋時間和計算的復(fù)雜性，需要對這些指紋按正確和一致的方式分類，以便輸入指紋時只需要在數(shù)據(jù)庫中找到其中匹配的一個子集而已。指紋分類是一種可以預(yù)先分配一個指紋進入已經(jīng)分類好的幾個類型之中并提供一種分度裝置的技術(shù)。指紋分類可以看作是大概的粗糙的指紋類型的相配。一個輸入指紋首先對預(yù)先存儲的類型大致相配，然后,在詳細的分析，把他和只包含那一個類型指紋的數(shù)據(jù)庫的子集相比較。我們正在研究一種運算法則——把指紋分類為五個類型，即螺旋狀紋、右回旋，左回旋，拱形，帳篷形。運算法則被一個Gabor過濾器分開過濾為四個方向(0度、45度、90度和135度)。這些數(shù)據(jù)被量化產(chǎn)生作為分類的指紋編碼。我們的分類以一個二階段的分類器為基礎(chǔ)。第一個階段用一個K最近的分類器和第二個階段用一組類神經(jīng)網(wǎng)路分類器。在分類器NIST-4數(shù)據(jù)庫中測試了4,000個圖像。對于五種類型的問題，分類的準(zhǔn)確性達到90%。對于四種類型的問題(拱門和帳篷型組合在一個類型之內(nèi))，我們能夠達成94.8%的分類準(zhǔn)確性。當(dāng)合并不合格者選項時，分類準(zhǔn)確性能達到為五種分類時增加到96%和至97.8%，為四種類型時30.8%的被拒絕。指紋圖像增強：在自動指紋識別系統(tǒng)中一個重要的步驟是從輸入的指紋圖像中自動地而且可靠地吸取指紋細節(jié)。然而，這種獲取指紋細節(jié)的計算法則很嚴(yán)重地依賴輸入指紋圖