Biometrics: Personal Identification

1. Biometrics: Personal Identification BIOM 426: Biometrics Systems Instructor: Natalia Schmid

2. Outline • Introduction • Applications • Identification methods • Requirements to biometrics • Biometrics technology • Automatic Identification: • design • representation • feature extraction • matching • evaluation • Privacy Issues

3. Introduction • Identification: associating identity with an individual. • Two types of identification problems: • verification (confirming or denying person's identity) Am I who I claim I am? • identification or recognition (establishing identity) Who am I? password PIN

4. Introduction Facts: • Master Card: estimated fraud at 450 million per year • 1 billion dollars worth of calls are made by cellular bandwidth thieves • ATM related fraud - 3 billion annually • 3,000 illegal immigrants crossing the Mexican border each day

5. Identification methods Person’s identity is everything what person represents and believes. Engineering approach: reduce the problem to: (i)some possession ("something what he has") or (ii) some knowledge ("something what he knows") Another approach: reduce it to a problem of authentication based on physical characteristics (physiological or behavioral). Definition: Biometrics are person's identification based on his/her physiological or behavioral characteristics. "something that you are"

6. New Definition Biometrics are automated methods of recognizing a person based on a physiological or behavioral characteristic. (BCC2003)

7. Requirements to biometrics 1. universality: everyone should have it 2. uniqueness: small probability that two persons are the same in terms of this characteristic 3. permanence: invariance with the time 4. collectability: can be measured quantitatively 5. performance: high identification accuracy 6. acceptability: acceptance by people 7. circumvention: how easy to fool the system by fraudulent technique

8. Accepted Biometrics Accepted and studied biometrics: voice, hand geometry, gait, fingerprint, ear, face, iris, retina, fingerprint, infrared facial and hand vein thermograms, key stroke, signature, DNA DNA, signature, and fingerprint are recognized in court of law

9. Biometric Technology: Overview Fingerprints:are graphical flow-like ridges. Their formation depends on embrionic development. Factors: (i) genetic, (ii) environmental Fingerprint acquisition: (i) scanning inked impression, (ii) life-scan Major representations: image, ridges, minutia (features derived from ridges), or pores Basic approaches to identification: (i) correlation based; (ii) global ridge patterns (classes); (iii) ridge patterns; (iv) fingerprint minutiae (ridge endings and bifurcations)

10. Biometric Technology: Overview Face:one of the most acceptable biometrics Two identification approaches: (i) transform (eigenvalues, analysis of covariance matrix, orthonormal basis vectors) (ii)attribute-based approach (geometirc features) Factors that influence recognition: (i) facial disguise (ii) facial expressions (iii) lighting conditions (iv) pose variation

11. Biometric Technology: Overview Iris:is one of the most reliable biometrics. Frontal images are obtained using near infrared camera (320 x 480 pixels) at distance < 1 meter. Iris images are (i) segmented and (ii) encoded. Twins have different iris patterns.

12. Biometric Technology: Overview Voice:is a behavioral characteristic and is not sufficiently unique (large database). Processing: signal subdivided into a few frequency bands. The most commonly used feature is cepstral feature (log of FT in each band). Matching strategies: hidden Markoff, vector quantization, etc. Types of verification: text-dependent; text-independent; language-independent. Voice print is highly accepted biometrics. Used for identification over the telephone. Easy to fool the system.

13. Biometric Technology: Overview Infrared Facial and Hand Vein Thermogram: Human bodies radiate heat. Infrared sensors acquire an image of heat distribution along the body. Images = thermograms. Imaging methods: similar to visible spectrum photographs. Processing: raw images are normalized with respect to heat radiating from landmark features. In uncontrolled environment, other sources of heat could be disturbance.

14. Biometric Technology: Overview Gait:is the specific way one walks.Complex spatio-temporal behavioral characteristic. Gait is not unique and does not stay invariant over time. It is influences by: distribution of body weight, injuries involving joints or brain, aging. Gait features are derived from a video sequence and consists of charactertization of several movements (computer vision problem).

15. Biometric Technology: Overview Retinal Scan: Retinal vasculature isrich in structure. Unique characteristic of each individual and each eye. Not easy to change or replicate. Image capture: requires person to peep into an eye-piece and focus on a specific spot. A predetermined part of retinal vasculature is imaged. Requires cooperation. Not accepted by public. Can reveal some medical conditions as hypertension.

16. Biometric Technology: Overview Signature:the way person signs his/her name. Highly acceptable behavioral biometrics. Evolves over time and depends on physical and mental conditions. Easily forged. Modeling the invariance and automating signature recognition process is challenging. Two approaches to signature verification: (i) static (geometric features = strokes) (ii) dynamic (strokes and acceleration, velocity, trajectory)

17. Biometric Technology: Overview Hand and finger geometry:is used for access control (50% of market). System captures frontal and side views of palm. Measurements: length and width of fingers, various distances. The representation requirements are only 9 bytes. Hand geometry is not unique but highly acceptable.

18. Comparison of Biometrics Technologies From “Biometrics: Personal Identification in Networked Society,” p. 16

19. Automatic Identification History : • Prehistoric Chinese used thumb- ; print for identification • Alphonse Bertillon’s System of Anthropometric Identification (1882) is based on bodily measuments, physical description, and photographs. • Henry’s fingeprint classification 1685 system (1880) classifies in > 100 classes. rules Sets of are developed for: (i) matching of biometrics (ii) searching databases Automatic identification is due to: inexpensive computer resources, advances in computer vision, pattern recognition, and image understanding.

20. Applications • Civil applications: • Banking (electronic funds transfer, ATM security, Internet commerce, credit card transactions) • Physical access control (airport) • Information system security (access to databases via login) • Customs and immigration (identification based on hand geometry) • Voter/driver registration • Telecommunications (cellular bandwidth access control)

21. Example

22. Automatic Identification Design Identification system operates in two modes: (i) enrollment mode and (ii) identification. Enrollment Biometric Feature Extractor Reader Identification Feature Extractor Biometric Reader Feature Matcher

23. Automatic Identification Enrollment mode: - biometric measurement is captured - information from raw data extracted; - (feature, person) information is stored; - ID is issued (for verification). Identification mode: - biometric is sensed (live-scan); - features are extracted from the raw data; - match is performed (search of the database). In verification mode, person presents ID. Then system performs match only against one template in the database.

24. Recognition System Architecture of a typical pattern recognition system (see A. K. Jain, et al., p. 22).

25. Design Issues Given the speed, accuracy, and cost specifications 1. How to collect the input data? (3D, 2D, multiple views, high or low resolution) 2. Internal representation (features) for automatic feature extraction 3. How to extract features? (Algorithms, etc.) 4. How to select the "matching" metric? (Measurements are made in specific space) 5. How to implement it? 6. Organization of database 7. Effective methods for searching a template in the database (binning, etc.)

26. Acquisition Quality of collected data determines performance of the entire system. Associated tasks: (i) quality assessment (ii) segmentation (separation of the data into foreground and background). Research efforts: (i) richer data (3D, color, etc.) (ii) metrics for assessment quality of measurements. (iii) realistic models Solutions: enhancement

27. Representation • Which machine-readable representation captures the invariant and • discriminatory information in the data? • Determine features s.t. • - invariant for the same individual (intraclass variation) • - maximally distinct for different individuals (interclass) • More distincive features offer more reliable identification. • Representation has to be storage space efficient (smart card: 2 Kbytes) • Representation depends on biometrics

28. Feature extraction • Given raw data, automatically extracting the given representation is difficult problem. • Example: • manual fingerprint system uses about a dozen of features. For • automatic system, many of them are not easy to reliably detect. • Feature extraction procedures are typically designed in ad hoc manner (inefficient when measurements are noisy). • Determining effective models for features will help to reliably • extract them (esp. in noisy situations).

29. Matching Similarity metric should be robust against: - noise, - structural and statistical variations, - aging, and artifacts of feature extraction module. Example: signature (hard to define the ground truth) Performance is determined by: (i) representation and (ii) similarity metric. Trade-off: better engineering design vs. more complex matcher. Example: fingerprint (variations in features and rigid matcher vs. Flexible matcher)

30. Matching (Fingerprint) Sources of distortion and noise: (i) inconsistent contact (3D-to-2D) (ii) non-uniform contact (due to dryness of skin, sweat, dirt, humidity in the air, etc.) (iii) irreproducible contact (injuries to the finger) (iv) feature extraction artifacts (measurement error) (v) sensing itself adds noise

31. Evaluation An end-user questions: (i) Does the system makes an accurate identification? (ii) Is the system sufficiently fast? (iii) What is the cost of the system? Because of noise, distortions, and limited information no metric is adequate for reliable identification. Decisions: - genuine individual, - imposter.

32. Evaluation Four types of outcomes: (1) Genuine individual is accepted (true) (2) Genuine individual is rejected (error) (3) Imposter is rejected (true) (4) Imposter is accepted (error) FAR - false acceptance rate FRR - false rejection rate EER - equal error rate Given a database, performance is a RV and only can be estimated.

33. Evaluation Measure of performance: ROC - receiveroperating curve Confidence Intervals

34. Useful Links • http://www.biometrics.org/ • (publications and periodicals; • research and databases; meetings and events) • http://www.itl.nist.gov/div895/biometrics/ • http://biometrics.cse.msu.edu/ • http://www.tech.purdue.edu/it/resources/biometrics/ • http://www.wvu.edu/~bknc/ • http://www.citer.wvu.edu/