Biometrics Tasanawan Soonklang

2 Biometrics Biometrics – what is? Applications – who use? Operation – how does it work? Types – what are the different? Issues – how to choose?, accuracy, concerns IT related to biometrics Movies – some funMovies References – some more readings & linksreadings & 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. fingerprints Another big class, historically the first, is the identification for police application. police Now, some countries are using biometrics for immigration control in airport/border patrol. immigration Banks are now proposing some ATMs.ATM Payment using biometrics is more and more used in stores. stores Identification of the student in schools. schools Identification of the mother/newborn in hospitals.

14 Operation

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

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 :

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 Example Image processing a) The original b) A close-up of the original c) After 1 st stage of thinning d) After 2 nd stage of thinning e) After applying algorithm, showing bifurcations (black) and endpoints (grey) Original source :

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) Gattaca It was wrong – The Island (2005) The Island

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 ‘123’ of Biometric Technology Jain, Anil, Bolle, Ruud, and Pankanti, Sharath. (1999) Introduction to biometrics. In: Biometrics, Personal Identification in Networked Society, pp. 1-41, Springer. Introduction to biometrics Jain, Anil, and Ross, Arun. (1999) Introduction to biometrics. In: Handbook of Biometrics, ppIntroduction to biometrics Lecture notes Ioannis Pavlidis. (2003) Introduction to biometrics. In course cosc6397. Department of Computer Science, University of Houston.Introduction to biometrics Rawitat Pulum. (2006) Introduction to Biometrics. In course Faculty of Science, Silpakorn University.Introduction to Biometrics Website orange.fr/fingerchip/biometrics/biometrics.htmhttp://pagesperso- orange.fr/fingerchip/biometrics/biometrics.htm

49 Relation

50 Relation The more lower EER, the more accuracy