biometrics cubs, university at buffalo govind/cse717 [email protected]

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Biometrics CUBS, University at Buffalo [email protected]

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  • Slide 1
  • Biometrics CUBS, University at Buffalo [email protected]
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  • Conventional Security Measures Possession or Token Based Passport, IDs, Keys License,Smart cards,Swipe cards, Credit Cards Knowledge Based Username/password PIN Combination(P,K) ATM Disadvantages of Conventional Measures Do not authenticate the user Tokens can be lost or misused Passwords can be forgotten Multiple tokens and passwords difficult to manage Repudiation
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  • Biometrics Definition Biometrics is the science of verifying and establishing the identity of an individual through physiological features or behavioral traits Examples Physical Biometrics Fingerprint, Hand Geometry,Iris,Face Measurement Biometric Dependent on environment/interaction Behavioral Biometrics Handwriting, Signature, Speech, Gait Performance/Temporal biometric Dependent on state of mind Chemical Biometrics DNA, blood-glucose
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  • Requirements of Biometrics Universality Each person should have the biometric Uniqueness Any two persons should have distinctive characteristics Permanence Characteristic should be invariant over time Collectability Characteristic should be easy to acquire Acceptability Is non-intrusive Non repudiation User cannot deny having accessed the system
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  • General Biometric System Database Biometric Sensor Feature Extraction Biometric Sensor Feature Extraction Matching ID : 8809 Authentication Enrollment Result
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  • Types of Authentication Verification Answers the question Am I whom I claim to be? Identity of the user is known 1:1 matching Identification Answers the question Who am I? Identity of the user is not known 1:N matching Positive Recognition Determines if an individual is in the database Prevents multiple users from assuming same identity Negative Recognition Determines if an individual is NOT in the given database Prevents single user from assuming multiple indentities
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  • Aspects of a Biometric Systems Sensor and devices Types of sensors Electrical and mechanical design Feature representation and matching Enhancement, preprocessing Developing invariant representations Developing matching algorithms Evaluation Testing System Issues Large Scale databases Securing Biometric Systems Ethical, Legal and Privacy Issues
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  • Applications And Scope of Biometrics TechnologiesHorizontal ApplicationsKey Vertical Markets FingerprintCivil IDGovernment Sector Facial RecognitionSurveillance and ScreeningTravel and Transportation Iris ScanPC / Network AccessFinancial Sector MiddlewareRetail / ATM / Point of SaleHealth Care AFISCriminal IDLaw Enforcement Voice ScaneCommerce / Telephony Hand GeometryPhysical Access / Time and Attendance Signature Verification Keystroke Dynamics
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  • Biometric Modalities Common modalities Iris Fingerprint Face Voice Verification Hand Geometry Signature Other modalities Retinal Scan Odor Gait Keystroke dynamics Ear recognition Lip movement
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  • Fingerprint Verification Fingerprints can be classified based on the ridge flow pattern Fingerprints can be distinguished based on the ridge characteristics
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  • Feature Extraction XYT 10626320R 15350335R 25581215B
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  • Matching XYT 10626320R 15350335R 25581215B XYT 08120R 21320145R 37246109B T(X, Y, )? Rotation Scaling Translation Elastic distortion
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  • Face Recognition:Eigen faces approach Eigen facesNormalization Face detection and localization
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  • Face Feature Representations Facial Parameters Semantic modelEigen faces
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  • Speaker Recognition Speaker IdentificationSpeaker VerificationSpeaker Detection Text Dependent Text Independent Text Dependent Text Independent Forensics Caller identification Speech Codecs IVR Computer Access Transactions over phone
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  • Cepstral feature approach Silence Removal Cepstrum Coefficients Cepstral NormalizationLong time average Polynomial Function Expansion Dynamic Time Warping Distance Computation Reference Template Preprocessing Feature Extraction Speaker model Matching
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  • Vocal Tract modeling Signal SpectrumSmoothened Signal SpectrumSpeech signal
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  • Speaker Model F 1 = [a1a10,b1b10] F 2 = [a1a10,b1b10] F N = [a1a10,b1b10] .
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  • Signature Verification Off line Signature Verification Online Signature verification
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  • Simple Regression Model Similarity by R 2 : 91% R2=R2= Y = (y 1, y 2, , y n ) X = (x 1, x 2, , x n ) Matching Similarity Measure Similarity by R 2 : 31%
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  • DTW warping path in a n-by-m matrix is the path which has min cumulative cost. The unmarked area is the constrain that path is allowed to go. ( y 2 is matched x 2, x 3, so we extend it to be two points in Y sequence.) Similarity = R 2 Dynamic Alignment Where (x 1i, y 1i, v 1i ) are points in the sequence And a, b, c are the weights, e.g., 0.5, 0.5, 0.25 Dynamic alignment
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  • Iris Recognition Sharbat Gula:The Afghan GirlIriscode used to verify the match
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  • Iris Recognition Choosing the bits Gabor Kernel Iris Image
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  • Collage
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  • Hand Geometry
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  • Evaluation of Biometric Systems Technology Evaluation Compare competing algorithms All algorithms evaluated on a single database Repeatable FVC2002, FRVT2002, SVC2004 etc. Scenario Evaluation Overall performance Each system has its own device but same subjects Models real world environment Operational Evaluation Not easily repeatable Each system is tested against its own population
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  • System Errors FAR/FMR(False Acceptance Ratio) FRR/FNMR(False Reject Ratio) FTE(Failure to Enroll) FTA(Failure to Authenticate) Genuine (w1) Impostor (w2) GenuineNo errorFalse Reject ImpostorFalse Accept No error Confusion matrix
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  • Performance Curves: Score Distribution
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  • Performance curves: FAR/FRR
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  • Performance curves: ROC
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  • State of the art BiometricsState of the artResearch Problems Fingerprint 0.15% FRR at 1% FAR (FVC 2002) Fingerprint Enhancement Partial fingerprint matching Face Recognition 10% FRR at 1% FAR (FRVT 2002) Improving accuracy Face alignment variation Handling lighting variations Hand Geometry 4% FRR at 0% FAR (Transport Security Administration Tests) Developing reliable models Identification problem Signature Verification 1.5%(IBM Israel) Developing offline verification systems Handling skillful forgeries Voice Verification