Biometrics

1. Biometrics CUBS, University at Buffalo http://www.cubs.buffalo.edu http://www.cedar.buffalo.edu/~govind/CSE717 govind@buffalo.edu

2. 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

3. 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

4. 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

5. ID : 8809 General Biometric System Biometric Sensor Feature Extraction Database Enrollment Feature Extraction Biometric Sensor Matching Authentication Result

6. 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

7. 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

8. Biometric Modalities • Common modalities • Iris • Fingerprint • Face • Voice Verification • Hand Geometry • Signature • Other modalities • Retinal Scan • Odor • Gait • Keystroke dynamics • Ear recognition • Lip movement

9. Fingerprint Verification Fingerprints can be classified based on the ridge flow pattern Fingerprints can be distinguished based on the ridge characteristics

10. Feature Extraction

11. Matching • Rotation • Scaling • Translation • Elastic distortion T(ΔX, ΔY , Δθ)?

12. Face Recognition:Eigen faces approach Face detection and localization Eigen faces Normalization

13. Face Feature Representations Facial Parameters Eigen faces Semantic model

14. Speaker Recognition Speaker Identification Speaker Detection Speaker Verification Text Dependent Text Independent Text Dependent Text Independent Speaker Recognition • Speech Codecs • IVR • Computer Access • Transactions over phone • Forensics • Caller identification

15. Cepstral feature approach Silence Removal Cepstrum Coefficients Cepstral Normalization Long time average Polynomial Function Expansion Reference Template Dynamic Time Warping Distance Computation • Preprocessing • Feature Extraction • Speaker model • Matching

16. Vocal Tract modeling Speech signal Signal Spectrum Smoothened Signal Spectrum

17. F1 = [a1…a10,b1…b10] F2 = [a1…a10,b1…b10] ……………. ……………. FN = [a1…a10,b1…b10] Speaker Model

18. Signature Verification Online Signature verification Off line Signature Verification

19. R2= Matching – Similarity Measure • Simple Regression Model Y = (y1 , y2 , …, yn) X = (x1 , x2 , …, xn) Similarity byR2 : 91% Similarity byR2 : 31%

20. Dynamic Alignment Similarity = R2 ( y2 ismatched x2, x3, so we extend it to be two points in Y sequence.) Dynamic alignment Where (x1i, y1i, v1i) are points in the sequence And a, b, c are the weights, e.g., 0.5, 0.5, 0.25 • 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.

21. Iris Recognition Sharbat Gula:The Afghan Girl Iriscode used to verify the match

22. Iris Recognition Iris Image Choosing the bits Gabor Kernel

23. Collage

24. Hand Geometry

25. 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

26. System Errors • FAR/FMR(False Acceptance Ratio) • FRR/FNMR(False Reject Ratio) • FTE(Failure to Enroll) • FTA(Failure to Authenticate) Confusion matrix

27. Performance Curves: Score Distribution

28. Performance curves: FAR/FRR

29. Performance curves: ROC

30. State of the art

31. Thank You ssc5@cedar.buffalo.edu