Biometric Authentication using wavelets and visual cryptography 2012 1 A SEMINAR REPORT ON BIOMETRIC AUTHENTICATION USING WAVELETS AND VISUAL CRYPTOGRAPHY SUBMITTED BY VINIT ANIL GAIKWAD ROLL NO. 42067 TE COMPUTER DIV. I SEMINAR GUIDE Prof. Mrs.S.S.Paygude Department of Computer Engineering MAHARASHTRA INSTITUTE OF TECHNOLOGY PUNE-411038 2011-2012 Biometric Authentication using wavelets and visual cryptography 2012 2 MAHARASHTRA ACADEMY OF ENGINEERING & EDUCATIONAL RESEARCHS MAHARASHTRA INSTITUTE OF TECHNOLOGY PUNE. DEPARTEMENT OF COMPUTER ENGINEERING MCERTIFICATE This is to certify thatVINIT ANIL GAIKWAD (42067) of T. E. Computer Engineering Div I successfully completed seminar in BIOMETRIC AUTHENTICATION USING WAVELETS AND VISUAL CRYPTOGRAPHY to my satisfaction and submitted the same during the academic year 2011-2012 towards the partial fulfillment of degree of Bachelor of Engineering in Computer Engineering of Pune University under the Department of Computer Engineering, Maharashtra Institute of Technology, Pune. Prof. S.S.Paygude (Seminar Guide and Head of Computer Engineering) Biometric Authentication using wavelets and visual cryptography 2012 3 ACKNOWLEDGEMENT I express my true sense of gratitude towards my seminar guide and Head of ComputerDepartmentProf.Mrs.S.S.Paygude,whoateverydiscretestepinthe studyofthisseminar,contributedwithhervaluableguidanceandprovidedwith perfect solutions for every problem that arose. I would also like to express my appreciation and thanks to all my friends who knowingly Or unknowingly assisted me with their valuable suggestion and comments and I am very grateful for their assistance.

Yours sincerely, GAIKWAD VINIT ANIL 42067 Biometric Authentication using wavelets and visual cryptography 2012 4 INDEX Page no. Chapter 1 : Biometrics 9 1.1: Introduction10 1.1.1 Opportunities 11 1.1.2 Qualification of biometrics11 1.2: Different Biometrics considered till date11 1.2.1 Voice11 1.2.2Infrared Facial and Hand Vein Thermograms12 1.2.3 Fingerprints15 1.2.4 Face 16 1.2.5 Iris17 1.2.6 Keystroke dynamics18 1.2.7 DNA18 1.2.8 Retinal Scan19 1.3 Iris Recognition 21 1.3.1 Introduction21 1.3.2 The Uniqueness of Iris 22 1.3.3 Locating of Iris23 1.3.4 Application for Iris recognition24 1.3.5 Conclusive Truth: Iris is the best way forward24 Biometric Authentication using wavelets and visual cryptography 2012 5

Chapter 2 : Security Issues in Biometric Authentication 25 2.1:Security Issues in BiometricAuthentication25 2.2: Attacks25 2.2.1 Spoof Attack 25 2.2.2 The reply attack 26 2.2.3 Data Simulation 26 2.3 Attacks on Digital Watermark 28 Chapter 3 : Halftone Visual Cryptography 29 3.1: Introduction 29 3.2: Mechanisms of Visual Cryptography 30 3.3: Biometric based authentication using wavelets and Visual Cryptography 31 3.3.1: Introduction31 3.3.2: Proposed Method31 3.3.3: A generic watermarking system33 3.3.4: Algorithm 36 3.3.5: Bit replacement procedure38 3.4: Experimental Results39 3.5: Conclusion41

Chapter 4 : Bibliography 41 Biometric Authentication using wavelets and visual cryptography 2012 6 Appendix A : Keywords and Meanings 30 Biometric Authentication using wavelets and visual cryptography 2012 7 Biometric Authentication using wavelets and visual cryptography 2012 8 CHAPTER 1 BIOMETRICS 1.1 Introduction Biometricsdealswithidentificationofindividualsbasedontheirbiologicalorbehavioral characteristics.Biometricshaslatelybeenreceivingattentioninpopularmedia.itiswidely believed that biometrics will become a significant component of the identification technology as (i) the prices of biometrics sensors continue to fall (ii) the underlying technology becomes more mature, and (iii)thepublicbecomesawareofthestrengthsandlimitationsofbiometrics.Thischapter providesanoverviewofthebiometricstechnologyanditsapplicationsandintroducesthe researchissuesunderlyingthebiometrics.Associatinganidentitywithanindividualiscalled personal identification. The problem of resolving the identity of a person can be categorized into two fundamentally distinct types of problems with different inherent complexities:(i)verification and(ii) recognition (more popularly known as identification1) Verification(authentication)referstotheproblemofconfirmingordenyingaperson'sclaimed identity(AmIwhoIclaimIam?).Identification(WhoamI?)referstotheproblemof establishingasubject'sidentity-eitherfromasetofalreadyknownidentities(closed identificationproblem)orotherwise(openidentificationproblem).Thetermpositivepersonal identificationtypicallyrefers(inbothverificationaswellasidentificationcontext)to identificationofapersonwithhighcertainty.Humanracehascomealongwaysinceits inceptioninsmalltribalprimitivesocietieswhereeverypersoninthecommunityknewevery otherperson.Intoday'scomplex,geographicallymobile,increasinglyelectronicallyinter-connected information society, accurate identification is becoming very important and the Problem of identifying a person is becomingever increasingly difficult.A number of situations requireanidentificationofapersoninoursociety:haveIseenthisapplicantbefore?Isthis person an employee of this company? Is this individual a citizen of this country? Many situations will even warrant identification of a person at the far end of a communication channel. 1.1.1 Opportunities Biometric Authentication using wavelets and visual cryptography 2012 9 Accurateidentificationofapersoncoulddetercrimeandfraud,streamlinebusinessprocesses, andsavecriticalresources.Hereareafewmindbogglingnumbers:about$1billiondollarsin welfare benefits in the United States are annually claimed by double dipping welfare recipients withfraudulentmultipleidentities.MasterCardestimatesthecreditcardfraudat$450million perannumwhichincludeschargesmadeonlostandstolencreditcards:unobtrusivepositive personalidentificationofthelegitimateownershipofacreditcardatthepointofsalewould greatly reduce the credit card fraud; about 1 billion dollars worth of cellular telephone callsaremadebythecellularbandwidththieves-manyofwhicharemadefromstolenpins and/orcellulartelephones.Again,anidentificationofthelegitimateownershipofthecellular telephoneswouldpreventcellulartelephonethievesfromstealingthebandwidth.Areliable methodofauthenticatinglegitimateownerofanATMcardwouldgreatlyreduceATMrelated fraudworthapproximately$3billionannually.Apositivemethodofidentifyingtherightful checkpayeewouldalsoreducebillionsofdollarsmisappropriatedthroughfraudulent encashment of checks each year. A method of positive authentication of each system login would eliminate illegal break-ins into traditionally secure (even federal government) computers. The UnitedStatesImmigrationandNaturalizationservicestipulatesthatitcouldeachday detect/deterabout3,000illegalimmigrantscrossingtheMexicanborderwithoutdelayingthe legitimatepeopleenteringtheUnitedStatesYetanotherapproachtopositiveidentificationhas been to reduce the problem of identification to the problem of identifying physical characteristics of the person. The characteristics could be either a person's physiological traits, e.g., fingerprints, hand geometry, etc. or her behavioral characteristics, e.g., voice and signature. This method ofidentificationofapersonbasedonhis/herphysiological/behavioralcharacteristicsiscalled biometrics.Theprimaryadvantageofsuchanidentificationmethodoverthemethodsof identification utilizing something thatyou possess or something thatyou know approach is thatabiometricscannotbemisplacedorforgotten;itrepresentsatangiblecomponentof somethingthatyouare.Whilebiometrictechniquesarenotanidentificationpanacea,they, especially,whencombinedwiththeothermethodsofidentification,arebeginningtoprovide very powerful tools for problems requiring positive identification. 1.1.2 Qualification of biometrics Biometric Authentication using wavelets and visual cryptography 2012 10 Whatbiologicalmeasurementsqualifytobeabiometric?Anyhumanphysiologicalor behavioral characteristic could be a biometrics provided it has the following desirable properties [15]:(i)universality,whichmeansthateverypersonshouldhavethecharacteristic,(ii) uniqueness,whichindicatesthatnotwopersonsshouldbethesameintermsofthe characteristic,(iii)permanence,whichmeansthatthecharacteristicshouldbeinvariantwith time,and(iv)collectability,whichindicatesthatthecharacteristiccanbemeasured quantitatively. In practice, there are some other important requirements [15,16]: (i) performance, whichreferstotheachievableidentificationaccuracy,theresourcerequirementstoachievean acceptableidentificationaccuracy,andtheworkingorenvironmentalfactorsthataffectthe Identificationaccuracy,(ii)acceptability,whichindicatestowhatextentpeoplearewillingto acceptthebiometricsystem,and(iii)circumvention,whichreferstohoweasyitistofoolthe system by fraudulent techniques. No single biometrics is expected toeffectively satisfy the needs of allidentification (authentication)applications.Anumberofbiometricshavebeenproposed,researched,and evaluatedforidentification(authentication)applications.Eachbiometricshasitsstrengthsand limitations. 1.2 Different Biometrics considered till date 1.2.1 Voice Voiceisacharacteristicofanindividual[17].However,itisnotexpectedtobesufficiently unique to permit identification ofan individual from a large database of identities. Moreover, a voicesignalavailableforauthenticationistypicallydegradedinqualitybythemicrophone, communicationchannel,anddigitizercharacteristics.Beforeextractingfeatures,theamplitude oftheinputsignalmaybenormalizedanddecomposedintoseveralband-passfrequency channels. The features extracted from each band may be either time-domain or frequency domain features. One of the most commonly used features is cepstral feature - which is a logarithm of the Fourier Transform of the voice signal in each band. The matching strategy may typically employ approaches based on hidden Markov model, vector quantization, or dynamic time warping [17]. Textdependentspeakerverificationauthenticatestheidentityofasubjectbasedonafixed predeterminedphrase.Text-independentspeakerverificationismoredifficultandverifiesa Biometric Authentication using wavelets and visual cryptography 2012 11 speaker identity independent of the phrase. Language independent speaker verification verifies a speaker identity irrespective of the language of the uttered phrase and is even more challenging. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 1000 2000 Figure1.2Voicesignalrepresentinganutteranceofthewordseven.XandYaxes represent time and signal amplitude, respectively. Voicecaptureisunobtrusiveandvoiceprintisanacceptablebiometricinalmostallsocieties. Some applications entail authentication of identity over telephone. In such situations, voice may betheonlyfeasiblebiometric.Voiceisabehavioralbiometricsandisaffectedbyaperson's health (e.g., cold), stress, emotions, etc. To extract features which remain invariant in such cases is very difficult. Besides, some people seem to be extraordinarily skilled in mimicking others. A reproductionofanearlierrecordedvoicecanbeusedtocircumventavoiceauthentication system in the remote unattended applications. One of the methods of combating this problem is to prompt the subject (whose identity is to be authenticated) to utter a different phrase each time. 1.2.2 Infrared Facial and Hand Vein Thermograms Biometric Authentication using wavelets and visual cryptography 2012 12 FigureVoicecaptureisunobtrusiveandvoiceprintisanacceptablebiometricinalmostallsocieties. Some applications entail authentication of identity over telephone. In such situations, voice may betheonlyfeasiblebiometric.Voiceisabehavioralbiometricsandisaffectedbyaperson's health (e.g., cold), stress emotions, etc. To extract features which remain invariant in such cases is very difficult. Besides, some people seem to be extraordinarily skilled in mimicking others. A reproductionofanearlierrecordedvoicecanbeusedtocircumventavoiceauthentication system in the remote unattended applications. One of the methods of combating this problem is to prompt the subject (whose identity is to be authenticated) to utter a different phrase each time. Biometric Authentication using wavelets and visual cryptography 2012 13 Figure 1.3 Identification based on facial thermograms [1]. The image is obtained by sensing the infrared radiations from the face of a person. The graylevel at each pixelischaracteristicofthemagnitudeoftheradiation.Humanbodyradiatesheatandthe patternofheatradiationisacharacteristicofeachindividualbody.Aninfraredsensorcould acquireanimageindicatingtheheatemanatingfromdifferentpartsofthebody.Theseimages are called thermograms. The method of acquisition of the thermal image unobtrusively is akin to the capture of a regular (visible spectrum) photograph of the person. Any part of the body could beusedforidentification.Theabsolutevaluesoftheheatradiationaredependentuponmany extraneousfactorsandarenotcompletelyinvarianttotheidentityofanindividual;theraw measurements of heat radiation need to be normalized, e.g., with respect to heat radiating from a landmark feature of the body. The technology could be used for covert identificationsolutionsandcoulddistinguishbetweenidenticaltwins.Itisalsoclaimedto provideenablingtechnologyforidentifyingpeopleundertheinfluenceofdrugs:theradiation patternscontainsignatureofeachnarcoticdrug.Athermogram-basedsystemmayhaveto addresssensingchallengesinuncontrolledenvironments,whereheatemanatingsurfacesinthe vicinity of the body, e.g., room heaters and vehicle exhaust pipes, may drastically affect the imageacquisitionphase.Infraredfacialthermogramsseemtobeacceptablesincetheir acquisition is a non-contact and non-invasive sensing technique. Biometric Authentication using wavelets and visual cryptography 2012 14 1.2.3 Fingerprints Fingerprints are graphical flow-like ridges present on human fingers. Their formations depend on theinitialconditionsoftheembryonicdevelopmentandtheyarebelievedtobeuniquetoeach person (and each finger). Fingerprints are one of the most mature biometric technologies used in forensicdivisionsworldwideforcriminalinvestigationsandtherefore,haveastigmaof criminality associated with them. Typically, a fingerprint image is captured in one of two ways: (i) scanning an inked impression of a finger or (ii) using a live-scan fingerprint scannerMajorrepresentationsofthefingerarebasedontheentireimage,fingerridges,orsalient featuresderivedfromtheridges(minutiae).Fourbasicapproachestoidentificationbasedon fingerprintareprevalent:(i)theinvariantpropertiesofthegrayscaleprofilesofthefingerprint image or a part thereof; (ii) global ridge patterns, also known as fingerprint classes; (iii) the ridge patternsofthefingerprints;(iv)fingerprintminutiaethefeaturesresultingmainlyfromridge endings and bifurcations. 1.2.4 Face Face is one of the most acceptable biometrics because it is one of the most common method of identification which humans use in their visual interactions. In addition, the method of acquiring faceimagesisnon-intrusive.Twoprimaryapproachestotheidentificationbasedonface recognition are the following: Biometric Authentication using wavelets and visual cryptography 2012 15 (i) Transform approach [20, 21]: the universe of face image domain is represented using a set of orthonormalbasisvectors.Currently,themostpopularbasisvectorsareeigenfaces:each eigenfaceisderivedfromthecovarianceanalysisofthefaceimagepopulation;twofacesare consideredtobeidenticaliftheyaresufficientlycloseintheeigenfacefeaturespace.A number of variants of such an approach exist. (ii)Attribute-basedapproach:facialattributeslikenose,eyes,etc.areextractedfromtheface imageandtheinvarianceofgeometricpropertiesamongthefacelandmarkfeaturesisusedfor recognizing features. Facial disguise is of concern in unattended authentication applications.Itis very challenging to developfacerecognitiontechniqueswhichcantoleratetheeffectsofaging,facialexpressions, slightvariationsintheimagingenvironmentandvariationsintheposeoffacewithrespectto camera (2D and 3D rotations) 1.2.5 Iris Visualtextureofthehumanirisisdeterminedbythechaoticmorphogeneticprocessesduring embryonicdevelopmentandispositedtobeuniqueforeachpersonandeacheye[24].Aniris imageistypicallycapturedusinganon-contactimagingprocess(Figure1.7).Theimageis obtainedusinganordinaryCCDcamerawitharesolutionof512dpi.Capturinganirisimage involves cooperation from the user, both to register the image of iris in the central imaging area andtoensurethattheirisisatapredetermineddistancefromthefocalplaneofthecamera.A position-invariantconstantlengthbytevectorfeatureisderivedfromanannularpartoftheiris imagebasedonitstexture.Theidentificationerrorrateusingiristechnologyisbelievedtobe extremely small and the constant length position invariant code permits an extremely fast method of iris recognition. Biometric Authentication using wavelets and visual cryptography 2012 16 1.2.5 Ear It is known that the shape of the ear and the structure of the cartilegenous tissue of the pinna are distinctive.Thefeaturesofaneararenotexpectedtobeuniquetoeachindividual.Theear recognition approaches are based on matching vectors of distances of salient points on the pinna from a landmark location on the ear. No commercial systems are available yet and authentication of individual identity based on ear recognition is still a research topic. Figure Biometric Authentication using wavelets and visual cryptography 2012 17 1.2.6 Keystroke Dynamics It is hypothesized that each person types on a keyboard in a characteristic way. This behavioral biometrics is not expected to be unique to each individual but it offers sufficient discriminatory information to permit identity authentication. Keystroke dynamics is a behavioral biometric; for some individuals, one may expect to observe a large variations from typical typing patterns. The keystrokes of a person using a system could be monitored unobtrusively as that person is keying inotherinformation.Keystrokedynamicfeaturesarebasedontimedurationsbetweenthe keystrokes.Somevariantsofidentityauthenticationusefeaturesbasedoninter-keydelaysas wellasdwelltimes-howlongapersonholdsdownakey.Typicalmatchingapproachesuseaneuralnetworkarchitecturetoassociateidentitywiththekeystrokedynamicsfeatures.Some commercial systems are already appearing in the market. 1.2.7 DNA DNA(DeoxyriboNucleicAcid)istheone-dimensionalultimateuniquecodeforone's individuality-exceptforthefactthatidenticaltwinshavetheidenticalDNApattern.Itis, Biometric Authentication using wavelets and visual cryptography 2012 18 however,currentlyusedmostlyinthecontextofforensicapplicationsforidentification.Three issues limit the utility of this biometrics for other applications: (i) contamination and sensitivity: it is easy to steal a piece of DNA from an unsuspecting subject to be subsequently abused for an ulterior purpose; (ii) automatic real-time identification issues: the present technology for genetic matchingisnotgearedforonlineunobtrusiveidentifications.MostofthehumanDNAis identicalfortheentirehumanspeciesandonlysomerelativelysmallnumberofspecific locations(polymorphicloci)onDNAexhibitindividualvariation.Thesevariationsare manifestedeitherinthenumberofrepetitionsofablockofbasesequence(length polymorphism)orintheminornon-functionalperturbationsofthebasesequence(sequence polymorphism). The processes involved in DNA based personal identification determine whether twoDNAsamplesoriginatefromthesame/differentindividual(s)basedonthedistinctive signature at one or more polymorphic loci.A major component of these processes now exist in theformofcumbersomechemicalmethods(wetprocesses)requiringanexpert'sskills.There does not seem to be any effort directed at a complete automation of all the processes.(iii) privacy issues: information about susceptibilities of a person to certain diseases could be gained from the DNA pattern and there is a concern that the unintended abuseofgeneticcode information may result in discrimination in e.g., hiring practices. Figure 1.10 DNA is double helix structure made of four bases: Adenine (A), Thymine (T),Cytosine(C),andGuanine(G)[4].Thesequenceofbasesisuniquetoeach individual(withtheexceptionofidenticaltwins)andcouldbeusedforpositiveperson identification. 1.2.8 Retinal Scan Theretinalvasculatureisrichinstructureandissupposedtobeacharacteristicofeach individualandeacheye.Itisclaimedtobethemostsecurebiometricssinceitisnoteasyto changeorreplicatetheretinalvasculature.Retinalscans,glamorizedinmoviesandmilitary installations,aremostlyresponsibleforthehigh-tech-expensiveimpressionofthebiometric technology.Theimagecapturerequiresapersontopeepintoaneye-pieceandfocusona specificspotinthevisualfieldsothatapredeterminedpartoftheretinalvasculaturecouldbe imaged.Theimageacquisitioninvolvescooperationofthesubject,entailscontactwiththe eyepiece, and requires a conscious effort on the part of the user. All these factors adversely affect Biometric Authentication using wavelets and visual cryptography 2012 19 thepublicacceptabilityofretinalbiometric.Anumberofretinalscan-basedidentity authenticationinstallationsareinoperationwhichboastzerofalsepositivesinallthe installationsto-date.Retinalvasculaturecanrevealsomemedicalconditions,Althoughiris scanningappearstobemoreexpensivethanretinalscanning.Thesesystemswereoperatingatan unknown high false negative rates. e.g.,hypertension,whichisanotherfactorstandinginthewayofpublicacceptanceofretinal scan based-biometrics. Biometric Authentication using wavelets and visual cryptography 2012 20 1.3 Iris Recognition 1.3.1 Introduction Iris recognition is not a new idea but has only been available in practical application for the last 10 to 15 years. This idea has been featured in many science fiction movies but until recently was just a theoretical concept. Iris recognition is used for security purposes and is an almost foolproof entry-level access security means because of its ability to readily identifyfalse irises (Henahan, 2002, 8). It has not been widely used because of the cost, but has applications that are ever Increasing.Irisrecognitionwillbeaviableoptionforanysecuritysysteminthefuture.Iris recognitionisabiometricthatdependsontheuniquenessoftheiris.Theirisisauniqueorgan that is composed of pigmented vessels and ligaments forming unique linear marks, slight ridges, grooves, furrows, vasculature, and other similar features and marks (Daugman, 2003a). Comparing more features of the iris increases the likelihood of uniqueness. Since more features are being measured, it is less probable for two irises to match. Another benefit of using the iris is its stability. The iris remains stable for a lifetime because it is not subjected to the environment, as it is protected by the cornea and aqueous humor. The process of iris recognition is complex. It begins by scanning a persons iris (Henahan, 2002, 6).The individual stares into a camera for Biometric Authentication using wavelets and visual cryptography 2012 21 at least a second allowing the camera to scan their iris. An algorithm processes the digital image created by the camera to locate the iris. Once the iris has been located, another algorithm encodes the iris into a phase code that is the 2048-bit binary representation of an iris (Daugman, 2003b).The phase code is then compared with a database of phase codes looking for a match. On a300MHzSunMicrosystemsprocessormorethan100,000iriscodescanbecomparedina second(Daugman,2003a).Inamatterofafewsecondsanindividualcanhavehis/hereyes scanned and matched to an iris code in a database identifying the individual. 1.3.2 The Uniqueness of the Iris Howcanwebesuretheirisisunique?Inanalyzingtheiristheremustbebitsofanirisphase codethatarestatisticallyindependent.Statisticallyindependentmeansaneventslikelihoodof occurrence is equally probable regardless of the outcome of a given event (Larsen & Marx,2001).ThestatisticalindependenceofaniriscanbedeterminedbyusingtheBoolean Exclusive-Or,XOR,andANDoperatorsontheirisphasebitsofanytwopatterns(Daugman, 2003b). XOR is a bit comparison operator that that will return 0 when comparing like bits andotherwisereturns1.ANDisalsoabitcomparisonoperatorthatwillreturn1onlywhen comparingbitsthatareboth1.TheXORoperationshowshowthetwoirispatternsdiffer,and the AND operation eliminates the effects of background noise in the image. The combination of the XOR operator with the AND operator to normalize the result produces a fractional Hamming distance. AfractionalHammingdistanceisusedtoquantifythedifferencebetweenirispatterns.The Hamming distance of two vectors is the numberof components in whichthe vectors differ in a particularvectorspace(Gallian,2002).Inthisinstance,thefractionalHammingdistancewill always be between 0 and 1. For iris patterns, the Hamming distance should theoretically be 0.500 because a bit has an equally likely chance of being 0 or 1 (Daugman, 2003b). Dr. John Daugman, a professor at Cambridge University, analyzed the Hamming distances by comparing over 4250 iris images. He found the distribution of Hamming distances to be a perfect binomial distribution withameanof0.499andastandarddeviationof0.0317.Abinomialdistributionisamodel based on a series of trials that have two possible outcomes (Larsen & Marx, 2001). The mean is the average of all measured values, while the standard deviation is amount that the values tend to vary from the mean. The observed maximum value was 0.664, and the observed minimum value Biometric Authentication using wavelets and visual cryptography 2012 22 was 0.334. This means that it highlyunlikelyfor two different irises toagree in more than two thirdsoftheirphasebits.Byasimplecalculation,thedegrees-of-freedomofthedistributionis 249. This demonstrates that of the 2048 bits, only a small number are mutually independent due tocorrespondingradialcomponentsthatexistwithinaniris.Thesefindingsdemonstratethe uniqueness of an iris using the Hamming distance as a measurement. Are the irises of two people with the same genetic makeup distinguishable? Thisisanimportantquestionbecauseitwoulddemonstrateapossiblepitfallinthisbiometric. This condition hinders DNA testing because identical twins, twins from the same embryo, yield the same results in a DNA test. Any given person has a genetically identical pair of left and right irisesthatcanbecompared(Daugman&Downing,2001).Inasimilaranalysisdoneby Daugman , 648 iris images from 324 people were subjected to the same conditions used to render aHammingdistance(2004).Themeanandstandarddeviationforthisanalysiswere0.497and 0.031,respectively.Thisstudywasrepeatedwiththeirisesfromidenticaltwinsandyieldeda similar result. These studies show that an individual has two unique irises, and a pair of twins has four unique irises. Thus, an iris image is independent of an identical genetic makeup. 1.3.3 Locating the Iris Theirisiscapturedinanimagebyacamera.Thecameraneedstobeabletophotographa pictureinthe700to900nanometersrangesothatitwillnotbedetectedbythepersonsiris during imaging (Daugman , 2003b) .The camera may or may not have a wide-angle lens yielding ahigherresolution,butineithercaseamirrorisusedtoutilizefeedbackfortheimage.These conditions must be met in order for the iris image to have the necessary 50-pixel minimum size of the iris radius. Oncetheimageoftheirisisobtained,theirisneedstobelocatedwithintheimage.Thereare three variables within the image that are needed to fully locate the iris: the center coordinates, the irisradius,andthepupilradius(Daugman,2003b).Analgorithmdeterminesthemaximum contourintegralderivativesusingthethreevariablestodefineapathofcontourintegrationfor each of the variables. The complex analysis of the algorithm finds the contour paths defining the outer and inner circumferences of the iris. Statistical estimation changes the circular paths of the integral derivatives to Biometric Authentication using wavelets and visual cryptography 2012 23 Arc - shaped paths that best fit both iris boundaries. 1.3.4 Applications for Iris Recognition Irisrecognitionhastremendouspotentialforsecurityinanyfield.Theirisisextremelyunique and cannot be artificially impersonated by a photograph (Daugman, 2003).This enables security to be able to restrict access to specific individuals. An iris is an internal organ making it immune to environmental effects. Since an iris does not change over the course of a lifetime, once an iris isencodeditdoesnotneedtobeupdated.Theonlydrawbacktoirisrecognitionasasecurity installmentisitsprice,whichwillonlydecreaseasitbecomesmorewidelyused.Arecent application of iris recognition has been in the transportation industry, most notably airline travel. Thesecurityadvantagesgivenbyirisrecognitionsoftwarehaveastrongpotentialtofix problems in transportation (Breault, 2005).Its most widely publicized use is in airport security. IBMandtheSchipholGroupengagedinajointventuretocreateaproductthatusesiris recognition to allow passengers to bypass airport security (IBM,2002, 5).This product is already beingusedinAmsterdam.AsimilarproducthasbeeninstalledinLondonsHeathrow,New YorksJFK,andWashingtonsDullesairports(Airport,2002,2&3).Thesemachines expeditetheprocessofpassengersgoingthroughairportsecurity,allowingtheairportstorun moreefficiently.Irisrecognitionisalsousedforimmigrationclearance,airlinecrewsecurity clearance,airportemployeeaccesstorestrictedareas,andasmeansofscreeningarriving passengersforalistofexpelledpersonsfromanation(Daugman,2005).Thistechnologyisin placeintheUnitedStates,GreatBritain,Germany,Canada,Japan,Italy,andtheUnitedArab Emirates. 1.3.5 Conclusive truth: Iris is the best way forward Irisrecognitionhasproventobeaveryusefulandversatilesecuritymeasure.Itisaquickand accuratewayofidentifyinganindividualwithnoroomforhumanerror.Irisrecognitionis widely used in the transportation industry and can have many applications in other fields where security is necessary. Its use has been successful with little to no exception, and iris recognition will prove to be a widely used security measure in the future. Biometric Authentication using wavelets and visual cryptography 2012 24 CHAPTER 2 SECURITY ISSUES IN BIOMETRIC AUTHENTICATION 2.1 Security Issues in Biometric Authentication Livingintheinformationage,millionsofpeopleusedigitaldevicesto communicatewitheachotherthroughtheInternet.Networksecurityplaysanincreasingly important role in our daily life. Many efforts have been made to develop an information system thatcanaccuratelyauthenticate,properlyauthorize,andefficientlyauditlegitimateusers. Among these activities, authentication is the first and most critical link in the security chain. Authenticationisaprocessthatverifiesausersidentity,whichcanbeaccomplishedbyusing oneormoreofthevalidationfactors-theknowledgefactor,thepossessionfactor,orthe biometricsfactor.Sincebiometricsistheonlyfactorthatisdirectlylinkedwiththe distinguishing characteristic of an individual, it has been advocated that biometric authentication willachieveincreasinglevelsofassuranceofidentityverification.Biometricauthentication referstoanysecuritysystemthatusesmeasurablehumanphysiologicalorbehavioral characteristics to verify identity. Ideally these characteristics should be measurable, unique to an individual,invariableovertime,andshouldnotbeeasilyduplicated.Unfortunately,recent research has shown that it is not very difficult to steal a biometric trait, create its copy, and use thefaketraittoattackbiometricsystems.Thisisaseriousproblemwhenpeopleintendtouse biometricsasameanstoenhancenetworksecuritybecausetheuserhastoberemotely authenticated through an open network. 2.2 Attacks Likeotherinformationsystems,biometricauthenticationsystemsarevulnerabletoattackand canbecompromisedatvariousstages.Besidesbeingvulnerabletocommonattackssuchas Biometric Authentication using wavelets and visual cryptography 2012 25 replayandman-in-the-middle,biometricsystemsare,inparticular,susceptibletospoofand template attacks. 2.2.1 Spoof attack Spoofingisanattackwhereamaliciousindividualpretendstobesomeoneelse.Inbiometrics, spoofingisaprocessthatdefeatsabiometricsystembyprovidingaforgedbiometriccopyof legitimateuser.Althoughspoofingtechniquesvarywithbiometrictechnologies,onethingthey haveincommonisthattheyallinvolvepresentingafakebiometricsampletothesensor. Therefore,itisnecessarytocaptureabiometricsamplefromalegitimateuser.Theartificially recreateddataisusedtoattackphysiologicalbiometrictechnologies,forinstance,byusinga fakefinger,substitutingahigh-resolutionirisimage,orpresentingafacemask.Besidesthe artifacts,mimicryisoftenusedtospoofbehavioralbiometrictechnologies.Theliveness detection is only applicable if mimicry is performed through a device. 2.2.2 The replay attack Itis another major threat to biometric authentication. It is performed by sending the previously submitteddataofalegitimateuserbacktotheauthenticator.Anattackercanobtainthedata either through a sniffer device or sniffer software during a successful authentication process, or by collecting a residual print left on the sensor after a successful authentication. In the first scenario, a recorded signal is reentered into the system by bypassing the biometric sensor. While inthesecondscenario,animageisresubmittedinthesamewaythatthelegitimateuserdid through the biometric sensor. To detect the reply attack, the authenticator has to ensure that the data is captured throughthe sensor,and has not been injected. Sensor noise and input variation makeitimpossiblethattherewillbeonehundredpercentsimilaritybetweenanytwosamples. Therefore,thispropertyhasbeenusedtorecognizereplayattacksbysomeproducts.Themost popularmethodiseitherbuildingatimestamporusingchallengeandresponsemechanismto address the reply attack. 2.2.3 Data simulation Biometric Authentication using wavelets and visual cryptography 2012 26 Sincebiometricfeaturesusedforidentificationandverificationarenotsecretand haveevenbeenpublishedontheInternet,animpostercanattackthebiometricsystemthrough data simulation. It is easy to understand that an imposter can simulate a legitimate users physical signature or mimic his or her speech to attack behavioral biometric systems. However, it would appear that an imposter needs a great deal of effort to generate a fingerprint pattern or create a face image to attack a physiological biometric system. Unfortunately, it has been reported that a commercially available thumbprint system was breached with synthesized thumb image, even thoughthesyntheticallygeneratedimagelookedverydifferentfromtheenrolledimage.When generating the thumb image, the most important concept is to position and orient the minutiae to formtheoverallappearanceofathumbprint.Astofacialrecognition,Adlerproposedan approach to reconstruct facial images so that an enrolled user in the system can be attacked with synthesized facial images. It works in the following way. An initial image is selected, and using the matching scores for each successiverecognition, the initial image is modified. Experimental resultsonthreecommercialfacerecognitionsystemsshowthathismethodneedsonlyseveral thousand iterations togenerate an image thatcan beconfused with the original image ata very highconfidencelevel.Sincethematchingscorewasusedasthedriverforthisattack,therisk can be mitigated by keeping the matching score inside the matcher and not releasing it to theenduser.Dependingonthesystemconfiguration,aman-in-themiddleattackispossible whilethedataisintransitfromonecomponenttoanother.Asshown,theattackercan manipulatetheinputdatastream;sendafaketemplateasanenrolleduser;injectanartificial matching score; or even generate a forged response. Several technologies can be implemented to reduce the threats of transmission-based attacks. Biometric Authentication using wavelets and visual cryptography 2012 27 2.3 Attacks on Digital Watermark Awatermarkattackisanattackondigitaldatatoidentifythehidden watermarkillegally.Theseattackshavetobetreatedcarefully,asthesuccessofany watermarking scheme depends on it. According to, watermark attacks can be classified into four main groups: (i)Simpleattacks:Thesetypesofattacksattempttodamagetheembedded watermarkbymodificationsofthewholeframewithoutanyefforttoidentify andisolatethewatermark.Examplesinclude frequencybasedcompression, addition of noise, cropping and correction. (ii)Detection-disabling attacks: These attempts to break correlation and to make detectionofthewatermarkimpossible.Geometricdistortionlikezooming, shiftinspatialor(incaseofvideo)temporaldirection,rotation,croppingor pixel permutation, removal or insertion are used. (iii)Ambiguityattacks:Theseattacksthedetectorbyproducingfake watermarkeddatatodiscredittheauthorityofthewatermarkbyembedding severaladditionalwatermarkssothatitisnotobviouswhichwasthefirst, authoritative watermark. Biometric Authentication using wavelets and visual cryptography 2012 28 (iv)Removalattacks:Theremovalattacksestimatesthewatermark,separateit out and discard only the watermark. Examples are collusion attack, denoising orexploitingconceptualcryptographicweaknessofthewatermarkscheme (e.g. knowledge of positions of single watermark elements). CHAPTER 3 HALFTONE VISUAL CRYPTOGRAPHY 3.1 Introduction Visualcryptographyencodesasecretbinaryimage(SI)intosharesof randombinarypatterns.Ifthesharesarexeroxedontotransparencies,thesecretimagecanbe visuallydecodedbysuperimposingaqualifiedsubsetoftransparencies,butnosecret information can be obtained from the superposition of a forbidden subset. The binary patterns of theshares,however,havenovisualmeaningandhindertheobjectivesofvisualcryptography. Extended visual cryptographywas proposed recently to construct meaningful binary images as sharesusinghypergraphcolourings,butthevisualqualityispoor.Inthispaper,anovel techniquenamedhalftonevisualcryptographyisproposedtoachievevisualcryptographyvia halftoning.Basedontheblue-noiseditheringprinciples,theproposedmethodutilizesthevoid and cluster algorithm [2] to encode a secret binary image into halftone shares (images) carrying significantvisualinformation.Thesimulationshowsthatthevisualqualityoftheobtained halftonesharesareobservablybetterthanthatattainedbyanyavailablevisualcryptography method known to date. Biometric Authentication using wavelets and visual cryptography 2012 29 VISUAL CRYPTOGRAPHY (VC) is a type of secret sharing scheme introduced by Naor.In a -out-of-schemeofVC,asecretbinaryimage(SI)iscryptographicallyencodedintosharesof random binary patterns. The shares are xeroxed onto transparencies, respectively, and distributed amongstparticipants,oneforeachparticipant.Noparticipantknowsthesharegiventoanother participant. Any or more participants can visually reveal the secret image by superimposing any transparencies together. The secret cannot be decoded by any or fewer participants, even if infinite computational power is available to them. Being a type of secret sharing scheme, visual cryptographycanbeusedinanumberofapplicationsincludingaccesscontrol.Forinstance,a bank vault must be opened every day by three tellers, but for security purposes, it is desirable not to entrust any single individual with the combination. Hence, a vault-access system that requires anytwoofthethreetellersmaybedesirable.Thisproblemcanbesolvedusingatwo-out-of-threethresholdscheme.Asidefromtheobviousapplicationtoaccesscontrol,secretsharing schemesareusedinanumberofothercryptographicprotocolsandapplicationssuchas thresholdcryptography,privatemultipartycomputations,electroniccashanddigitalelections. Morespecifically,visualthresholdschemeshavefoundimmediateapplicationsincertaintypes of cryptographic protocols, including authentication and identification , and copyright protection and watermarking Biometric Authentication using wavelets and visual cryptography 2012 30 3.2 Mechanism of Visual Cryptography ToillustratetheprinciplesofVC,considerthesimplesttwoout-of-twovisual thresholdschemewhereeachpixeloftheSIisencodedintoapairofsubpixelsineachofthe twoshares.Ifiswhite,oneofthetwocolumnstabulatedunderthewhitepixelinFig.1is selected. If is black, one of the two columns tabulated under the black pixel is selected. In each case, the selection is performed by randomly flipping a fair coin, such that each column has 50% probabilitytobechosen.Then,thefirsttwopairsofsubpixelsintheselectedcolumnare assigned to share 1 and share 2, respectively. Since, in each share, is encoded into a blackwhite orwhiteblackpairofsubpixelswithequalprobabilities,independentofwhetherisblackor white, an individual share gives no clue as to the value of . In addition, as each pixel is encrypted independently, no secret information can be gained by looking at groups of pixels in each share. Now consider the superposition of the two shares as shown in the last row .Ifa pixel is white, the superposition of the two shares always outputs one black and one white subpixel, no matter whichcolumnofsubpixelpairsischosenduringencoding.Ifisblack,ityieldstwoblack subpixels. There is a contrast loss in the reconstruction, however, the decoded pixel is readily visible. Biometric Authentication using wavelets and visual cryptography 2012 31 3.3 Biometric based authentication using wavelets and visual cryptography 3.3.1 Introduction Thedevelopmentofsophisticatedhardwareandsoftwareiscreatinga tremendous amount of information to be transmitted via World Wide Web and wireless networks.Mostoftheinformationbeingtransmittedhasmultimediacontentwhichare composed of image, text, video, sound, etc. Large part of this is composed of text and images. Modern ways of content sharing is easy and economical but introduces serious concernontheprotectionissues,thatis,individuals,otherthantheowner,may manipulate, duplicate or access media information illegally without the owners consent and knowledge. This has forced academicians, industrials and researchers to focus on protectionoftheirintellectualcontents.Severaltechniquesforthecontentprotection havebeenintroduced,whichincludesteganography,cryptographyandwatermarking. Outofallthesewatermarkingisatechniquethatisgainingmuchattention.Adigital watermarkisdefinedasinvisibleorinaudibledata(arandompatternofbitsornoise) permanentlyembeddedinagraphic,video,oraudiofileforprotectingcopyrightor authenticatingdata.Thewatermarkingtechniquesarebroadlycategorizedintorobust watermarking(copyrightprotection)andfragilewatermarking(multimediacontent authentication). A third type of watermarking technique that is becoming popular is the BiometricWatermarking.Biometricwatermarkingisatechniquethatcreatesalink between a human subject and the digital media by embedding biometric information into the digital object. A biometric is defined as life measure and biometric technology uses imagesofhumanbodyparts,capturedthroughcamerasandscanningimages. Watermarking techniques are increasingly used in biometric security systems. Biometriccharacteristicsareface,voiceprint,fingerprint,etc.Outof these, iris image is considered to be more reliable for personal authentication. They are consideredgoodchoicebecauseoftwoveryimportantcharacteristics,itsuniqueness Biometric Authentication using wavelets and visual cryptography 2012 32 andpermanency.ItisprovedstatisticallythatirisismoreaccuratethanevenDNA matchingastheprobabilityoftwoirisesbeingidenticalis1in10tothepowerof78. Duringtransmission,however,theyaresusceptibletoaccidentalandintentional attacks,whichemphasizetheneedforaprotectiveschemetopreservefidelityand preventalterations.Thispaperpresentsasecurewatermarkingschemethatstoresa secret message inside an cover image. The iris image is secured by using a technique calledVisualCryptography(VC).Themainobjectiveistopresentanewvisual cryptographicsystemwhichcanbeusedtohidebiometricimageandprotectthe biometric image from attacks. Visual cryptography method presents the Water marking system, explains the proposed method.Manyexperimentsconductedandsimulationresultsarepresentedhere. Finally, conclusions are given at the end. 3.3.2 Proposed method In1995NaorandShamirhavesuggestedforthefirsttimetosolvethe secretsharingproblembythemeansofnewcryptographicstructurecalledVisual Cryptography (VC). In the proposed approach the secret is divided into two shares, whichareprintedontothetwotransparencies(shares)andgiventotheparticipants. Only these two participants who possess the transparencies can reconstruct the secret by superposition of shares. One can not recover a secret without the other one. In the visualthresholdscheme,thesharesareimagesrepresentedontransparencies consisting of black and white (transparent, actually) pixels. The visual systems perform aBooleanoperation,whichiseasytovisualizeusingthe(2,2)VisualThreshold Scheme shown in. Laterin2001theengineersfromTaiwanintheirpaperhaveclaimedthat duringtheencodingprocesssharesaregeneratedinsuchawaythattheycontain randomdotstocreateachaosforpreventingintrudersofrandomguesswork.They proposetwoalgorithmsforsecretsharingandsecretrecoveryderivedfromtheleast significant bit substitution method. Thus generation of shares could be also done using so called cover images. In the next section we will explain this alternative algorithm for Biometric Authentication using wavelets and visual cryptography 2012 33 creating shares used by Tsai, Chang and Chen. Visual cryptographic has been applied tomanyapplications,includingbutnotrestrictedtoinformationhiding,generalaccess structures,visualauthenticationandidentification.Thesolutionsnormallyoperateon binaryinputs.Afteritsinitialintroduction,manyresearchershavefounddifferent variationsofVC.Theimprovementvariesfrombinaryimagetohalftoneimages,gray scale and color images. InhalftoneVC,thenatural(continuous-tone)imagesarefirstconvertedintohalftone images by using the density of the net dots to simulate the original gray or color levels in the target binary representation. The halftoning technique is used in many applications suchasfacsimile(FAX),electronicscanningandcopying,andlaserprintingetc. Verheul and Tilborgintroduced the concept of VC to color images. The disadvantage in thiswasthatthequalityoftherecoveredimagewaspoorandthesharingwas meaningless. This work motivated several others to produce more advanced schemes. Alltheseworksusedtechniqueswhereacoloredimagewashiddenintomultiple meaningfulcoverimages.Changetal.in2000introducedanewcoloredsecret sharingandhidingschemebasedonVisualCryptographyschemeswherethe traditionalstackingoperationofsubpixelsandrowsinterrelationswasmodified.This work was later enhancedto avoid the usage of CIT. Youmaranimproved this scheme to improve the quality of the cover images while achieving lossless recovery and without increasing the computational complexity of the algorithm. In 2003, Houproposed a Biometric Authentication using wavelets and visual cryptography 2012 34 methodforcolorVC,whereacolorimageisfirstdecomposedintoseveralindividual channels.Generalhalftonetechniqueisthenappliedtothesechannelstoaccomplish thecreationofshares.Youmaranlaterimprovedbyusingtheschemetohidea coloredimageintomultiplecoloredcoverimageswithoutdegradingthequalityofthe recoveredimage.Mostofthereviewedliteratureworkswithembeddingofimages (black and white, gray scale or color) into another cover image. In the present work, VC is used to store a secret message file inside a color image. For this purpose, a hybrid LSB and DWT VC method is proposed and is explained in the next section 3.3.3 A generic watermarking system The three main stages of any digital watermarking system shown inare watermark embedding, watermark extraction and watermark detection. During embedding process, an algorithm accepts the host and the data to be embedded and produces a watermarked signal. The watermarked signal is then transmitted to embeddedequipment.Theextractionordetectionalgorithmisusedtoextractthe hiddenwatermarkfromthetransmittedimage.Thewatermarkingschemesare generallyclassifiedintorobustwatermarkingschemesandfragilewatermarking schemes.Boththeschemesaredesignedfordifferentapplications.Amongthem, robustwatermarksaregenerallyusedforcopyrightprotectionandownership identificationbecausetheyaredesignedtowithstandattackssuchascommonimage processingoperations.Incontrast,fragilewatermarksaremainlyappliedtocontent authenticationandintegrityattestationbecausetheyarefragiletoattacks,i.e.,itcan detect any changes in an image as well as localizing the areas that have been changed. Biometric Authentication using wavelets and visual cryptography 2012 35 In robust watermarking applications, the extraction algorithm should be able to correctly producethewatermark,evenifthemodificationswerestrong.Infragilewatermarking, the extraction algorithm should fail if any change is made to the signal. The parameter used for image watermarking algorithm. Capacity,i.e.theamountofinformationthatcanbeputintothewatermarkand recovered without errors; Robustness, i.e. the resistance of the watermark to alterations of the original content such as compression, filtering or cropping; Visibility,i.e.howeasilythewatermarkcanbediscernedbytheuser.Thedesired propertiesarehighcapacity,lowdistortionandhighrobustnesstoattacksorhigh security.Thesefactorsareinter-dependent;forexample,increasingthecapacitywill decrease the robustness and increase the visibility. Therefore, it is essential to consider all three factors for a fair evaluation or comparison of watermarking algorithms. 3.3.4 Algorithm Theproposedworkperformsacolorvisualcryptographicschemeon biometric image. Since the biometric image needs to be very near to the original image, a meaningful VC method was not adopted. The process is explained below. The original coverimageisfirstconvertedintofourregionsusing1-DHaarDigitalwavelet transformation (DWT). The 1-D transformation of the image decomposes the image into four subbands, namely, HH, HL, LH and LL. Details regarding each subband is treated as a share. The secret message is stored in all the wavelet subbands and a modified LSBtechniqueisusedtoembedthesecretmessage.ThemodifiedLSBisshownin figureandtheprocedureisexplainedbelow.Thebestknownsteganographicmethod thatworksinthespatialdomainistheLSB(LeastSignificantBit),whichreplacesthe leastsignificantbitsofpixelsselectedtohidetheinformation.SLSB(SelectedLeast SignificantBit)improvestheperformanceofthemethodLSBhidinginformationby selectingonlyoneofthethreecolorsateachpixelofthecoverimagetohidethe message. To select the color it uses a Sample Pairs analysis and applies a LSB Match . Inthepresentwork,itisstoredinsidethefourwaveletsubbands.Thealgorithm choosesgreencolorpixelsandusesthethreeLSBofthepixeltostorethesecret message. Biometric Authentication using wavelets and visual cryptography 2012 36 The embedding is done in three steps. The first step performs an analysis of iris image to find a suitable representation that is small and easy to reconstruct. At the same time, the secret message is converted into a bit stream. The iris representation is embedded using a bit replacement procedure into Share1 (HH) , Share2 (HL), Share3 (LH) and Share4 (LL) sub bands. The final step performs a JPEG 2000 compression to combine the watermark and the image. The extraction performs a reverse process, the received imageisdecompressedandaninverseDWT(IDWT)isperformedtoreceivethefour shares.Thegreenplanesfromeachshareareconsideredandthesecretbitsare retrieved from the last two bits of each green pixel. Beforeretrievingthecolorinformation,theirisimageisanalyzedto extract its features. The iris image analysis is as follows: After acquiring an eye image Biometric Authentication using wavelets and visual cryptography 2012 37 throughdigitalcamera,theboundarybetweenthepupilandtheirisisdetectedafter position of the eye in the given image is localized. After the center and the radius of the pupil are extracted, the right and the left radius of the iris are searched based on these data.Byusingtheiriscenterandtheradiusthepolarcoordinatesystemisset,from whichthefeatureoftheirisisextracted.Thisistermedasiriscode.Thewavelet transformbreaksanimagedownintofoursubbandsorimages. Theresultsconsistof one image that has been high pass in the horizontal and vertical directions, one that has beenlowpassedintheverticalandhighpassedinthehorizontal,andonethathas been low pass filtered in both directions. The results of Haar transform in four types of coefficients:(i)Coefficientsthatresultfromaconvolutionwithginbothdirections(HH)represent diagonal features of the image. (ii)Coefficientsthatresult from aconvolution withgonthecolumnsafteraconvolution with h on the rows (HL) correspond to horizontal structures.(iii) Coefficients from high pass filtering on the rows, followed by low pass filtering of the columns (LH) reflect vertical information.(iv)The coefficients from low pass filtering in both directions are further processed in the next step(LL).

For the 450x60 iris image in polar coordinates, wavelet transform was applied 4 times in ordertogetthe28x3subimages(i.e.84features).Bycombiningthese84featuresin the HH sub-image of the highpass filter of the fourth transform (HH4) and each average value for the three remaining high-pass filters areas (HH1,HH2,HH3), the dimension of the resulting feature vector is 87. Each value of 87 dimensions has a real value between -1.0and1.0.Byquantizingeachrealvalueintobinaryformbyconvertthepositive value into 1 and the negative value into 0. Thus an iris can be represented with just 87 bit. 3.3.5 Bit Replacement Procedure Biometric Authentication using wavelets and visual cryptography 2012 38 A color image is made of three planes, namely, red,green and blue. In HVS, blue planesappear dark,red planeappearoverbrightand thereforegreen planes are chosen for embedding. The same approach was also used by [4]. In the presentwork,eachcoverpixelisdividedintotwobitsstrings:MostSignificantBits (MSN) and Least Significant Bits (LSB) as shown in the Figure 4. The SLSB are directly replaced by the secret data. 3.4 Experimental results Theproposedmodelwastestedwiththreecoverimagesnamely, Lena,BaboonandPepper,oneirisimageandonesecretimage.Thecoverand biometric image used along with secret message is shown in Figure 5. Itevaluatesthealgorithmbasedontheverificationaccuracyandqualityofdewater marked images of the models. The models were tested using 10 attacks, namely, JPEG Biometric Authentication using wavelets and visual cryptography 2012 39 with Quality Factor 50%, JPEG 2000 with Quality Factor 50%, Gaussian Noise (3 x 3), Median Filter (3 x 3), Blurring (3 x 3), Gamma (0.5), Cropping (10 pixels), Resize (90%), Rotation (10o) and Affine Transform. The system is also evaluated when no attacks was performed. TheresultsareprojectedintermsofPSNRofthecoverimagebeforeand after watermarking obtained and is presented in Figure 6. The extracted iris recognition wastestedforauthenticationwithanirisdatabasecreatedby.Thedatabasehas3x 128 iris images (3 x 64 left and 3 x 64 right). The images are 24 bit RGB images of 576 x 768 pixels in PNG file format. The recognition process was tested using a open source iris recognition system provided by Masek . Table 1 shows the verification results when the models were subjected to various attacks. Biometric Authentication using wavelets and visual cryptography 2012 40 It can be seen from the table, that when no attacks the system was able to produce the highest accuracy (99%). The performance of the slightly degrades with the affine transformationbutwillalltheotherattackstheaccuracyishighrangingbetween 81.55% and 98.84%. From the results obtained it can be concluded that the protection of iris image is highly successful while using the proposed method. The model produces high recognition results with high quality image after dewater marking. The results prove thattheproposedsystemisgoodforbiometricimage.Thesystemhastheadvantage thatthe dewatermarkedimageaswellthebiometricimagebothhavehighqualityand are resistant to various attacks. 3.5 Conclusion Digitalwatermarkingisanareawhichisusedforcopyrightprotectionofintellectual property and authentication. In the present work, a visual cryptographic way to store a biometricimageinsideacolorimageisproposed.Theoriginalityoftheschemeisto useawavelet1Ddecompositionandusethesubbandsasshareimages,where enhanced LSB technique is used to embed the iris data. During experimentation it was foundthatthesizeofthebiometricshouldbeless40%ofthecoverimage.And moreover,thesystemwasresistanttoseveralattacksbuttheperformanceslightly decreasedwiththeaffinetransformation.Futureresearchwilltaketheseinto consideration. Biometric Authentication using wavelets and visual cryptography 2012 41 CHAPTER 4 BIBLOGRAPHY (i). Introduction to biometrics Anil Jain Michigan State University East Lansing, MI jain@cse.msu.edu (ii). Iris Recognition: A General Overview Jesse Horst Undergraduate Student, Mathematics, Statistics, and Computer Science (iii) Biometric based authentication using wavelets and visual cryptography Mrs.D.Mathivadhani1, Dr.C.Meena2 Research Scholar, Centre Manager, Department of Computer Science, Department of Computer centre, AvinashilingamUniversity For Women, Coimbatore-641 043,Tamil Nadu 1 mathi_kirupha@yahoo.co.in 3meena_cc@avinuty.ac.in (iv) A Watermarking-based Visual Cryptography Scheme with Meaningful Shares HAN Yan-yan department of scientific research management Beijing electronic science & technology institute Beijing, China hyy@besti.edu.cn CHENG Xiao-ni school of telecommunications engineering Xidian university Biometric Authentication using wavelets and visual cryptography 2012 42 Xian China 0909420549@mail.besti.edu.cn HE Wen-cai communication engineering department Beijing electronic science & technology institute Beijing, China hwc@ besti.edu.cn (v)Fundamentals of image processingby Anil k Jain (vi)Digital Image processing by Gonzalez and Richard E. Woods