An Overview of Biometrics

1. An Overview of Biometrics Luciano Rila

2. Contents – biometric systems • Introduction • Biometric identifiers • Classification of biometrics methods • Biometric system architecture • Performance evaluation

3. Contentsbiometric technologies • Signature recognition • Voice recognition • Retinal scan • Iris scan • Face-scan and facial thermogram • Hand geometry

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

5. Personal identification objects • Token-based: “something that you have” • Knowledge-based: “something that you know” • Biometrics-based: “something that you are”

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

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

8. Biometric identifiers • Universality • Uniqueness • Stability • Collectability • Performance • Acceptability • Forge resistance

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

10. Other biometric methods • Found in the literature: • vein recognition (hand), • keystroke dynamics, • palmprint, • gait recognition, • body odour measurements, • ear shape.

11. Classification of biometrics methods • Static: • fingerprint • retinal scan • iris scan • hand geometry • Dynamic: • signature recognition • speaker recognition

12. Biometric system architecture • Basic modules of a biometric system: • Data acquisition • Feature extraction • Matching • Decision • Storage

13. Biometric system model

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

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

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

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

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

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

20. Possible decision outcomes • A genuine individual is accepted. • A genuine individual is rejected (error). • An impostor is rejected. • An impostor is accepted (error).

21. Errors • 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’). • Application dependent trade-off between two error types.

22. Pass rates

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

24. Biometric technologies • Signature recognition • Voice recognition • Retinal scan • Iris scan • Face biometrics • Hand geometry

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

26. Dynamic signature recognition • Variety of characteristics can be used: • angle of the pen, • pressure of the pen, • total signing time, • velocity and acceleration, • geometry.

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

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

29. Features of fingerprints

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

31. Fingerprint authentication • Basic steps for fingerprint authentication: • Image acquisition, • Noise reduction, • Image enhancement, • Feature extraction, • Matching.

32. Fingerprint processing • Original • Orientation • Binarised • Thinned • Minutiae • Minutia graph

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

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

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

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

37. Eye biometric • Iris: • coloured portion of the eye surrounding the pupil. • complex iris pattern used for identification. • Retina: • back inside of the eye ball. • pattern of blood vessels used for identification.

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

39. Retinal scan: advantages  disadvantages • Advantages: • High accuracy • Long-term stability • Fast verification • Disadvantages: • Difficult to use • Intrusive • Limited applications

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

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

42. The iris code

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

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

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

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

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

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

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

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