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1、PatternRecognitionLetters33(2012)984–990ContentslistsavailableatSciVerseScienceDirectPatternRecognitionLettersjournalhomepage:www.elsevier.com/locate/patrecAfusionapproachtounconstrainedirisrecognitiona,⇑bGilSantos,EdmundoHoyleaDepartmentofComputerScience,IT–InstitutodeTelecomunicações,UniversityofB
2、eiraInterior,Covilhã,PortugalbDepartmentofElectricalEngineering,FederalUniversityofRiodeJaneiro,RiodeJaneiro,BrazilarticleinfoabstractArticlehistory:Asbiometricshasevolved,theirishasremainedapreferredtraitbecauseitsuniqueness,lifetimestabilityAvailableonline7September2011andregularshapecontributetog
3、oodsegmentationandrecognitionperformance.However,commer-ciallydeployedsystemsarecharacterizedbystrongacquisitionconstraintsbasedonactivesubjectcoop-Keywords:eration,whichisnotalwaysachievableorevenreasonableforextensivedeploymentineverydayNonidealirisimagesscenarios.Researchonnewtechniqueshasbeenfoc
4、usedonloweringtheseconstraintswithoutsignifi-Irisrecognitioncantlyimpactingperformancewhileincreasingsystemusability,andnewapproacheshaverapidlyUnconstrainedbiometricsemerged.HereweproposeanovelfusionofdifferentrecognitionapproachesanddescribehowitcanDecisionfusioncontributetomorereliablenoncooperati
5、veirisrecognitionbycompensatingfordegradedimagescap-turedinlessconstrainedacquisitionsetupsandprotocolsundervisiblewavelengthsandvaryinglightingconditions.TheproposedmethodwastestedattheNICE.II(NoisyIrisChallengeEvaluation–Part2)con-test,anditsperformancewascorroboratedbyathird-placefinish.Ó2011Elsev
6、ierB.V.Allrightsreserved.1.IntroductionTherecognitiontechniquesusedinourpropositioncanbedi-videintwomaincategories.Inoneapproach,weusewavelet-Theuseoftheirisasmainbiometrictraithasemergedasoneofbasediris-feature-extractionmethods,complementedwithathemostrecommendedmethodsduenotonlytothepossibilityof
7、zero-crossingrepresentation(Hoyleetal.,2010,2009)andthenoncontactdataacquisitionandtoitscircularandplanarshapethatanalysisofiriscode-matchingbitdistribution(SantosandProença,facilitatesdetection,segme
