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Biometrics

Biometrics. Tasanawan Soonklang. Biometrics. Biometrics – what is? Applications – who use? Operation – how does it work? Types – what are the different? Issues – how to choose? , accuracy, concerns IT r elated to biometrics Movies – some fun

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Biometrics

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  1. Biometrics Tasanawan Soonklang

  2. Biometrics • Biometrics – what is? • Applications – who use? • Operation – how does it work? • Types – what are the different? • Issues –howto choose?, accuracy, concerns • IT related to biometrics • Movies– some fun • References –some more readings & links

  3. Biometrics

  4. What is ? • A term derived from ancient Greek bio = life metric = to measure • “Measurement of physiological and behavioral characteristics to automatically identify people.”

  5. Definition • “The automated approach to authenticate the identity of a person using the individual’s unique physiological or behavioral characteristics.” – Yau Wei Yun (2003) • “Biometrics deals with identification of individuals based on their biological or behavioral characteristics” – Jain et al (1999)

  6. Characteristics • Physical/biological characteristics • Face • Fingerprint • DNA • Hand and finger geometry • Eye structure • Iris • Retina • Ear • Vascular patterns • Odor • Voiceprint

  7. Characteristics • Behavioral characteristics • Signature • Gait • Handwriting • Keystroke • Voice pattern

  8. Identification • Identification – associating an identity with an individual • Verification (authentication) • The problem of confirming or denying a person’s claimed identity (1: 1) • Am I who I claim I am? • Recognition (identification) • The problem of establishing a subject’s identity (1: Many) • Who am I?

  9. Identification Methods • Traditional • Something you know: PIN, password... • Something you have: key, token, card... But does not insure that you are here and the real owner. • Biometrics • Something you are: a biometric.

  10. Applications

  11. Why use ? • Accurate identification of a person could deter • crime and fraud • streamline business processes • save critical resources

  12. Who uses ? • Government • Military • Schools • Commerce • Law Enforcement • Others ?

  13. Where are it used ? • Many products such as PC are already using fingerprints. • Another big class, historically the first, is the identification for policeapplication. • Now, some countries are using biometrics for immigration controlin airport/border patrol. • Banks are now proposing some ATMs. • Payment using biometrics is more and more used in stores. • Identification of the student in schools. • Identification of the mother/newborn in hospitals.

  14. Operation

  15. No Match Compare Match ? Process Capture Verification How does it work ? Enrollment Store Process Capture

  16. Example Original source : Anil Jain and Arun Ross (1999)

  17. Types

  18. Examples • Fingerprinting • Palm print • Iris scan • Retinal scan • Facial recognition • Voice recognition • Handwriting recognition • DNA

  19. Fingerprint • Strength • Proven Technology Capable of High Level of Accuracy • Range of Deployment Environments • Ergonomic, Easy-to-Use Device • Ability to Enroll Multiple Fingers • Weakness • Inability to Enroll Some Users • Performance Deterioration over Time • Association with Forensic Application • Need to Deploy Specialized Devices

  20. Palm print • Strength • Ability to Operate in Challenging Environment • Established, Reliable Core Technology • General Perception as Non-intrusive • Relatively Stable Physiological Characteristic as Basis • Combination of Convenience and Deterrence • Weakness • Inherently Limited Accuracy • Form Factor That Limits Scope of Potential Applications • Price

  21. Iris • Strength • Resistance to False Matching • Stability of Characteristic over Lifetime • Suitability for Logical and Physical Access • Weakness • Difficulty of Usage • False Non-matching and Failure-to-Enroll • User Discomfort with Eye-Based Technology • Need for a Proprietary Acquisition Device

  22. Retina • Strength • it is not easy to change or replicate the retinal vasculature. • Supposed to be the most secure biometric • Weakness • The image acquisition involves cooperation of the subject • entails contact with the eyepiece • requires a conscious effort on the part of the user.

  23. Face • Strength • Ability to Leverage Existing Equipment and Image Processing • Ability to Operate without Physical Contact or User Complicity • Ability to Enroll Static Images • Weakness • Acquisition Environment Effect on Matching Accuracy • Changes in Physiological Characteristics That Reduce Matching Accuracy • Potential for Privacy Abuse Due to Non-cooperative Enrollment and Identification

  24. Voice • Strength • Ability to Leverage Existing Telephony Infrastructure • Synergy with Speech Recognition and Verbal Account Authentication • Resistance to Imposters • Lack of Negative Perceptions Associated with Other Biometrics • Weakness • Effect of Acquisition Devices and Ambient Noise on Accuracy • Perception of Low Accuracy • Lack of Suitability for Today’s PC Usage

  25. Signature • Strength • Resistant to Imposters • Leverages Existing Processes • Perceived as Non-invasive • Users Can Change Signatures • Weakness • Inconsistent Signatures Lead to Increased Error Rates • Users Unaccustomed to Singing on Tablets • Limited Applications

  26. DNA • DNA (DeoxyriboNucleic Acid) is the 1D ultimate unique code for one’s individuality. • Identification for forensic applications only. • Three factors limit the utility of this biometric for other applications • Contamination and sensitivity • Automatic real-time identification issues • Privacy issues

  27. Issues

  28. Comparison • Universality– each person should have the characteristic. • Uniqueness– is how well the biometric separates individuals from another. • Permanence – measures how well a biometric resists aging. • Collectability – ease of acquisition for measurement. • Performance – accuracy, speed, and robustness of technology used. • Acceptability– degree of approval of a technology. • Circumvention – ease of use of a substitute.

  29. Comparison Original source : Yau Wei Yun (2003)

  30. How to choose ? • How to choose • Size of user group • Place of use and the nature of use • Ease of use and user training required • Error incidence such as due to age, environment and health condition • Security and accuracy requirement needed • User acceptance level, privacy and anonymity • Long term stability including technology maturity, standard, interoperability and technical support • Cost

  31. Accuracy • Failure to Enroll Rate (FTE) • % of data input is considered invalid and fails to input into the system. • False Acceptance Rate (FAR) • % of invalid users who are incorrectly accepted as genuine users. • False Rejection Rate (FRR) • % of valid users who are rejected as imposters. • Equal Error Rate (EER) • The rate at which both accept and reject error are equal

  32. FTE

  33. Scores & Threshold • scores – to express the similarity between a pattern and a biometric template.

  34. FAR & FRR

  35. Relation The more lower EER, the more accuracy Original source : http://www.bioid.com/sdk/docs/About_EER.htm

  36. Concerns • Identify theft and privacy • Using two-factor solution • Biometrics are purely based on matching • Using encryption for matching template • Scanned live biometric data maybe stolen • Sociological concerns • Physical harm to an individual • Personal information through biometric methods can be misused or sold

  37. Related to

  38. Example • Database • Storing matching templates • Querying templates • Database management • Security issues

  39. Example • Image processing • Assessing the quality • Enhancing the image

  40. Image processing The original A close-up of the original After 1st stage of thinning After 2nd stage of thinning After applying algorithm, showing bifurcations (black) and endpoints (grey) Example Original source : http://www.ee.ryerson.ca/opr/research_projects/graph_fingerprint.html

  41. Example • Intelligent system • Pattern classification & recognition • Decision rules

  42. Example • Pattern classification & recognition • Training and testing data • Machine learning Original source : Anil Jain and Arun Ross (1999)

  43. Example • Information retrieval • Retrieval templates for recognition • Scoring • Evaluation Recognition

  44. Movies

  45. Some fun • Hollywood is using biometrics for years. • some truth inside, but sometimes, it is wrong… • Must see • Gattaca(1997) • It was wrong • The Island (2005)

  46. Some fun • Others • James bond • The Bourne • Minority report • etc. (see the first website in reference) • Use of some and public concerns • Physical biometric for identification or authentication person is the most widely seen. • Behavioral biometric much less

  47. References

  48. More readings & links Publications • Yun, Yau Wei. (2003) The ‘123’ of Biometric Technology. - Retrieved from www. • Jain, Anil, Bolle, Ruud, and Pankanti, Sharath. (1999) Introduction to biometrics. In: Biometrics, Personal Identification in Networked Society, pp. 1-41, Springer. • Jain, Anil, and Ross, Arun. (1999) Introduction to biometrics. In: Handbook of Biometrics, pp Lecture notes • Ioannis Pavlidis. (2003) Introduction to biometrics. In course cosc6397.Department of Computer Science, University of Houston. • Rawitat Pulum. (2006) Introduction to Biometrics. In course 510670. Faculty of Science, Silpakorn University. Website • http://pagesperso-orange.fr/fingerchip/biometrics/biometrics.htm • http://en.wikipedia.org/wiki/Biometrics • http://www.bioid.com/sdk/docs/About_EER.htm

  49. Relation

  50. Relation The more lower EER, the more accuracy

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