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Biometrics

Biometrics. Viktor MINKIN minkin@elsys.ru. Outline. Outline Introduction Biometric systems Biometric characteristics Fingerprints Unimodal systems Multi-modal systems Problems Links History and future. Introduction. Biometrics [harmonized] Automated recognition of persons based on

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Biometrics

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  1. Biometrics Viktor MINKIN minkin@elsys.ru

  2. Outline • Outline • Introduction • Biometric systems • Biometric characteristics • Fingerprints • Unimodal systems • Multi-modal systems • Problems • Links • History and future

  3. Introduction Biometrics [harmonized] Automated recognition of persons based on their biological or/and behavioral characteristics. Automated measurement of biological or/and behavioral characteristics of person for medical, security or psychological purposes.

  4. Introduction • Terms and definitions Template Capture Comparison Database Enrollment Matching Token User

  5. Introduction • Identification of a person • Verification/Verify • Comparing one to one • “Am I who I claim I am” • Identification • Comparing one to many • “Who am I”

  6. Introduction • Application • Passport control • Access to secured areas • Surveillance • ATMs • Computer logins • E-commerce • Medicine • Psychology

  7. Introduction • Traditional means of automatic identification (before biometrics) • Knowledge-based • Use “something that you know” • Examples: password, PIN • Token-based • Use “something that you have” • Examples: credit card, smart card, keys

  8. Introduction • Problems with traditional approaches • Token may be lost, stolen or forgotten • PIN may be forgotten or guessed by the imposters • (25% of people seem to write their PIN on their ATM card) • Estimates of annual identity fraud damages per year: • $1 billion in welfare disbursements • $1 billion in credit card transactions • $1 billion in fraudulent cellular phone use • $3 billion in ATM withdrawals

  9. Introduction The traditional approaches are unable to differentiate between an authorized person and an imposter • Use biometrics which relies on “who you are” or • “what you do”

  10. Biometric Systems • Requirements for an ideal biometric • Universality • Each person should have the characteristic • Uniqueness • No two persons should be the same in terms of the characteristic • Permanence • The characteristic should not change

  11. Biometric Systems • Issues in a real biometric system • Performance • Identification accuracy, speed, robustness, resource requirements • Acceptability • Extend to which people are willing to accept a particular biometric identifier • Faked protection • How easy is it to fool the system by fraudulent methods

  12. Biometric Systems • Identification accuracy • FAR = false acceptance rate • FRR = false rejection rate • EER = equal error rate • TER = total error rate = FAR + FRR • FER= false enrollment rate

  13. Biometric Systems False Acceptance Rate Equal Error Rate False Rejection Rate Receiver operating characteristics (ROC)

  14. Biometric Systems FAR/FRR and comparison threshold

  15. Biometric Characteristics Static (biological) parameters • Fingerprints • Face • Iris • Hand geometry / vein • Retinal pattern • Facial thermogram • Lip information • DNA

  16. Biometric Characteristics Dynamic (behavior) biometric parameters • Signature • Voice • Motion • Pulse

  17. Biometric Characteristics Market Shares

  18. Biometric Characteristics Market development

  19. Fingerprints • Accurate • Comparatively cheap hardware • Questionable acceptance

  20. Fingerprints Light source Finger Prism Lens Video Camera (CCD) Light reflects from the surface of the prism where the finger is not in contact with it, while it penetrates the surface of the prism where the finger touches the surface of the prism. The resulting image goes through a lens into a video camera. Optical technology

  21. Fingerprints Capacity technology

  22. Fingerprints Fiber optic technology

  23. Fingerprints Minutia types BridgeDot Ridge Ending Bifurcation Enclosure Fingerprint types Arches Loops Whorl

  24. Fingerprints Core & Deltas

  25. Fingerprints Fingerprint minutiae

  26. Fingerprints Source FFT Flow field Directional Directional Directional image 1 image 2 irregularity Code Smoothing Binarization Skeleton Skeleton Minutiae formation cleaning search Image transformation

  27. Fingerprints Comparative testing

  28. Fingerprints Fingerprint information

  29. Unimodal Systems Illumination Head pose Occlusion Facial ID

  30. Unimodal Systems Questionable accuracy Hand Vein Hand geometry

  31. Unimodal Systems Retinal Pattern Highest accuracy Even more intrusive than iris recognition

  32. Unimodal Systems Facial Thermo image and VibraImage Non-intrusive Lie detection View-dependent Emotion control Depends heavily on Criminals detector human factors, Medical monitoring body temperature Psychology testing

  33. Multi-modal Systems • Why multimodal [multiple] person identification? • Quest for non-intrusive identification methods • No special purpose hardware needed • Works potentially at greater distances • “Traditional” arguments for going multimodal: • Increasing performance • Increasing robustness • Mono-modal recognition techniques are likely to reach in a close future a saturation in performance.

  34. Multi-modal Systems: Fusion Features Modality n-1 Features Modality 1 Features Modality 2 Features Modality n Classifier Identity “Early integration” or “sensor fusion” Integration is performed on the feature level Classification is done on the combined feature vector

  35. Multi-modal Systems 3 -Elsys includes BiCard, VibraImage, BioFinger 3D-Elsys is biological and behavioral identification system

  36. Multi-modal Systems The World population in 2000 was about 6.000 M. people. The biometric document (ID card) market is more than $6.000.000.000 There are 3 different ID card technologies: 1. Card with additional memory (chip, CD,..) 2. Card with 2d-bar code 3. BiCard (3D-Elsys)

  37. Problems • Errors rate • Misunderstanding of real advantages and problems • Incomplete true about biometric systems

  38. Links • International Biometric Group - http://www.biometricgroup.com • NIST - http://www.itl.nist.gov/div893/biometrics/ • Literature • http://www.itl.nist.gov/iaui/894.03/pubs.html#fing • Patents - http://www.elsys.ru/patents.php

  39. Biometrics evolution • 19 century- not automated identification • 20 century- biometric identification • 21 century- emotion recognition and detection

  40. Viktor Minkin Biometrics minkin@elsys.ru Thank you! 2004

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