1. Luciano Rila/RHUL1 An Overview of Biometrics Luciano Rila

2. Luciano Rila/RHUL2 Contents – biometric systems 1. Introduction 2. Biometric identifiers 3. Classification of biometrics methods 4. Biometric system architecture 5. Performance evaluation

3. Luciano Rila/RHUL3 Contents biometric technologies 6. Signature recognition 7. Voice recognition 8. Retinal scan 9. Iris scan 10. Face-scan and facial thermogram 11. Hand geometry

4. Luciano Rila/RHUL4 Personal identification Association of an individual with an identity: n Verification (or authentication): confirms or denies a claimed identity. n Identification (or recognition): establishes the identity of a subject (usually from a set of enrolled persons).

5. Luciano Rila/RHUL5 Personal identification objects n Token-based: “something that you have” n Knowledge-based: “something that you know” n Biometrics-based: “something that you are”

6. Luciano Rila/RHUL6 Biometrics Bio + metrics: The statistical measurement of biological data. -- Biometric Consortium definition: Automatically recognising a person using distinguishing traits.

7. Luciano Rila/RHUL7 Some applications n Financial security (e-fund transfers, ATM, e- commerce, e-purse, credit cards), n Physical access control, n Benefits distribution, n Customs and immigration, n National ID systems, n Voter and driver registration, n Telecommunications (mobile, TV)

8. Luciano Rila/RHUL8 Biometric identifiers n Universality n Uniqueness n Stability n Collectability n Performance n Acceptability n Forge resistance

9. Luciano Rila/RHUL9 Biometric technologies n Covered in ISO/IEC 27N2949: – recognition of signatures, – fingerprint analysis, – speaker recognition, – retinal scan, – iris scan, – face recognition, – hand geometry.

10. Luciano Rila/RHUL10 Other biometric methods n Found in the literature: – vein recognition (hand), – keystroke dynamics, – palmprint, – gait recognition, – body odour measurements, – ear shape.

11. Luciano Rila/RHUL11 Classification of biometrics methods n Static: – fingerprint – retinal scan – iris scan – hand geometry n Dynamic: – signature recognition – speaker recognition

12. Luciano Rila/RHUL12 Biometric system architecture n Basic modules of a biometric system: – Data acquisition – Feature extraction – Matching – Decision – Storage

13. Luciano Rila/RHUL13 Biometric system model

14. Luciano Rila/RHUL14 Data acquisition module n Reads the biometric info from the user. n Examples: video camera, fingerprint scanner/sensor, microphone, etc. All sensors in a given system must be similar to ensure recognition at any location. n Environmental conditions may affect their performance.

15. Luciano Rila/RHUL15 Feature extraction module n Discriminating features extracted from the raw biometric data. n Raw data transformed into small set of bytes – storage and matching. n Various ways of extracting the features. n Pre-processing of raw data usually necessary.

16. Luciano Rila/RHUL16 Matching module n The core of the biometric system. n Measures the similarity of the claimant’s sample with a reference template. n Typical methods: distance metrics, probabilistic measures, neural networks, etc. n The result: a number known as match score.

17. Luciano Rila/RHUL17 Decision module n Interprets the match score from the matching module. n Typically a binary decision: yes or no. n May require more than one submitted samples to reach a decision: 1 out of 3. n May reject a legitimate claimant or accept an impostor.

18. Luciano Rila/RHUL18 Storage module n Maintains the templates for enrolled users. n One or more templates for each user. n The templates may be stored in: – a special component in the biometric device, – conventional computer database, – portable memories such as smartcards.

19. Luciano Rila/RHUL19 Enrolment n Capturing, processing and storing of the biometric template. n Crucial for the system performance. n Requirements for enrolment: – secure enrolment procedure, – check of template quality and “matchability”, – binding of the biometric template to the enrollee.

20. Luciano Rila/RHUL20 Possible decision outcomes n A genuine individual is accepted. n A genuine individual is rejected (error). n An impostor is rejected. n An impostor is accepted (error).

21. Luciano Rila/RHUL21 Errors n Balance needed between 2 types of error: –Type I: system fails to recognise valid user (‘false non-match’ or ‘false rejection’). –Type II: system accepts impostor (‘false match’ or ‘false acceptance’). n Application dependent trade-off between two error types.

22. Luciano Rila/RHUL22 Pass rates

23. Luciano Rila/RHUL23 Tolerance threshold n Error tolerance threshold is crucial and application dependent. n Tolerance too large gives Type II error (admit impostors). n Tolerance too small gives Type I errors (reject legitimate users). n Equal error rate for comparison: false non- match equal to false match.

24. Luciano Rila/RHUL24 Biometric technologies n Signature recognition n Voice recognition n Retinal scan n Iris scan n Face biometrics n Hand geometry

25. Luciano Rila/RHUL25 Signature recognition n Signatures in wide use for many years. n Signature generating process a trained reflex - imitation difficult especially ‘in real time’. n Automatic signature recognition measures the dynamics of the signing process.

26. Luciano Rila/RHUL26 Dynamic signature recognition n Variety of characteristics can be used: –angle of the pen, –pressure of the pen, –total signing time, –velocity and acceleration, – geometry.

27. Luciano Rila/RHUL27 Signature recognition: advantages  disadvantages n Advantages: –Resistance to forgery –Widely accepted –Non-intrusive –No record of the signature n Disadvantages: –Signature inconsistencies –Difficult to use –Large templates (1K to 3K)

28. Luciano Rila/RHUL28 Fingerprint recognition n Ridge patterns on fingers uniquely identify people. n Classification scheme devised in 1890s. n Major features: arch, loop, whorl. n Each fingerprint has at least one of the major features and many ‘small’ features.

29. Luciano Rila/RHUL29 Features of fingerprints

30. Luciano Rila/RHUL30 Fingerprint recognition (cont.) n In a machine system, reader must minimise image rotation. n Look for minutiae and compare. n Minor injuries a problem. n Automatic systems can not be defrauded by detached real fingers.

31. Luciano Rila/RHUL31 Fingerprint authentication n Basic steps for fingerprint authentication: – Image acquisition, – Noise reduction, – Image enhancement, – Feature extraction, – Matching.

32. Luciano Rila/RHUL32 Fingerprint processing a)Original b) Orientation c) Binarised d) Thinned e) Minutiae f) Minutia graph

33. Luciano Rila/RHUL33 Fingerprint recognition: advantages  disadvantages n Advantages: –Mature technology –Easy to use/non- intrusive –High accuracy –Long-term stability –Ability to enrol multiple fingers n Disadvantages: –Inability to enrol some users –Affected by skin condition –Association with forensic applications

34. Luciano Rila/RHUL34 Speaker recognition n Linguistic and speaker dependent acoustic patterns. n Speaker’s patterns reflect: – anatomy (size and shape of mouth and throat), – behavioral (voice pitch, speaking style). n Heavy signal processing involved (spectral analysis, periodicity, etc)

35. Luciano Rila/RHUL35 Speaker recognition systems n Text-dependent: predetermined set of phrases for enrolment and identification. n Text-prompted: fixed set of words, but user prompted to avoid recorded attacks. n Text-independent: free speech, more difficult to accomplish.

36. Luciano Rila/RHUL36 Speaker recognition: advantages  disadvantages n Advantages: –Use of existing telephony infrastruct –Easy to use/non- intrusive/hands free –No negative association n Disadvantages: –Pre-recorded attack –Variability of the voice –Affected by noise –Large template (5K to 10K)

37. Luciano Rila/RHUL37 Eye biometric n Retina: – back inside of the eye ball. – pattern of blood vessels used for identification. n Iris: – coloured portion of the eye surrounding the pupil. – complex iris pattern used for identification.

38. Luciano Rila/RHUL38 Retinal pattern n Accurate biometric measure. n Genetically independent: identical twins have different retinal pattern. n Highly protected, internal organ of the eye. n May change during the life of a person.

39. Luciano Rila/RHUL39 Retinal scan: advantages  disadvantages n Advantages: –High accuracy –Long-term stability –Fast verification n Disadvantages: –Difficult to use –Intrusive –Limited applications

40. Luciano Rila/RHUL40 Iris properties n Iris pattern possesses a high degree of randomness: extremely accurate biometric. n Genetically independent: identical twins have different iris pattern. n Stable throughout life. n Highly protected, internal organ of the eye. n Patterns can be acquired from a distance (1m). n Patterns can be encoded into 256 bytes.

41. Luciano Rila/RHUL41 Iris recognition n Iris code developed by John Daugman at Cambridge. n Extremely low error rates. n Fast processing. n Monitoring of pupils oscillation to prevent fraud. n Monitoring of reflections from the moist cornea of the living eye.

42. Luciano Rila/RHUL42 The iris code

43. Luciano Rila/RHUL43 Iris recognition: advantages  disadvantages n Advantages: –High accuracy –Long term stability –Nearly non-intrusive –Fast processing n Disadvantages: –Not exactly easy to use –High false non- match rates –High cost

44. Luciano Rila/RHUL44 Face-scan and facial thermograms n Static controlled or dynamic uncontrolled shots. n Visible spectrum or infrared (thermograms). n Non-invasive, hands-free, and widely accepted. n Questionable discriminatory capability.

45. Luciano Rila/RHUL45 Face recognition n Visible spectrum: inexpensive. n Most popular approaches: – eigenfaces, – Local feature analysis. n Affected by pose, expression, hairstyle, make-up, lighting, eyeglasses. n Not a reliable biometric measure.

46. Luciano Rila/RHUL46 Face recognition: advantages  disadvantages n Advantages: –Non-intrusive –Low cost –Ability to operate covertly n Disadvantages: –Affected by appearance/environment –High false non-match rates –Identical twins attack –Potential for privacy abuse

47. Luciano Rila/RHUL47 Facial thermogram n Captures the heat emission patterns derived from the blood vessels under the skin. n Infrared camera: unaffected by external changes (even plastic surgery!) or lighting. n Unique but accuracy questionable. n Affected by emotional and health state.

48. Luciano Rila/RHUL48 Facial thermogram: advantages  disadvantages n Advantages: –Non-intrusive –Stable –Not affected by external changes –Identical twins resistant –Ability to operate covertly n Disadvantages: –High cost (infrared camera) –New technology –Potential for privacy abuse

49. Luciano Rila/RHUL49 Hand geometry n Features: dimensions and shape of the hand, fingers, and knuckles as well as their relative locations. n Two images taken: one from the top and one from the side.

50. Luciano Rila/RHUL50 Hand geometry: advantages  disadvantages n Advantages: –Not affected by environment –Mature technology –Non-intrusive –Relatively stable n Disadvantages: –Low accuracy –High cost –Relatively large readers –Difficult to use for some users (arthritis, missing fingers or large hands)

51. Luciano Rila/RHUL51 Choosing the biometrics n Does the application need identification or authentication? n Is the collection point attended or unattended? n Are the users used to the biometrics? n Is the application covert or overt?

52. Luciano Rila/RHUL52 Choosing the biometrics (cont.) n Are the subjects cooperative or non- cooperative? n What are the storage requirement constraints? n How strict are the performance requirements? n What types of biometrics are acceptable to the users?

53. Luciano Rila/RHUL53 References n ISO/DIS 21352: Biometric information management and security, ISO/IEC JTC 1/SC 27 N2949. n Scheuermann, Schwiderski-Grosche, and Struif, “Usability of Biometrics in Relation to Electronic Signatures”, GMD Report 118, Nov. 2000. n Jain et al., “Biometrics: Personal Identification in Networked Society,” Kluwer Academic Publishers. n Nanavati et al., “Biometrics: Identity Verification in a Networked Society,” Wiley. n The Biometric Consortium: http://www.biometrics.org/http://www.biometrics.org/

54. Luciano Rila/RHUL54 Any comments or questions? luciano.rila@rhul.ac.uk