Introduction to Biometrics

1. Introduction to Biometrics Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #5 Issues on Designing Biometric Systems September 7, 2005

2. Outline • Biometric Terms • Biometric Processes • Accuracy of Biometric Systems

3. Biometric Terms • Automated Use • Computers / machines used to verify or determine identity • Physiological/Behavioral Characteristic • Physiological: Identification based on physical properties such as finger scan, iris scan, - - - • Behavioral: e.g., identification based on gestures • Identity • A person may have multiple identities such as finger scan and face scan • Biometric • E.g., face scan is a biometric • Biometric system • Integrated hardware and software to perform verification and identification

4. Biometric Terms: Verification and Identification • Verification • User claims an identity for biometric comparison • User then provides biometric data • System tries to match the user’s biometric with the large number of biometric data in the database • Determines whether there is a match or a no match • Network security utilizes this process • Identification • User does not claim an identity, but gives biometric data • System searches the database to see if the biometric provided is stored in the database • Positive or negative identification • Prevents from enrolling twice for claims • Used to enter buildings

5. Biometric Terms: Logical vs. Physical Access • Physical Access Systems • Monitor, restrict and grant access to a particular area • E.g., time reporting, access to safe, etc. • Logical access systems • Restrict or grant access to information systems • E.g., popular for B2B and B2C systems

6. Biometric Process • User enrolls in a system and provides biometric data • Data is converted into a template • Later on user provides biometric data for verification or identification • The latter biometric data is converted into a template • The verification/identification template is compared with the enrollment template • The result of the match is specified as a confidence level • The confidence level is compared to the threshold level • If the confidence score exceeds the threshold, then there is a match • If not, there is no match

7. Example Template a1ij b1ij - - - - - - - - - - - r1ij Tij = a2ij b2ij - - - - - - - - - - - r2ij anij bnij - - - - - - - - - - - rnij Tij is the jth synthetic template created by the attacking system for user i

8. Biometric Process: Examplehttp://www.foodserve.com/Biometrics%20Defined.pdf • Step 1: Finger is scanned and viewed by the MorphoTouch (Sagem Morpho Inc.) access unit at the point of entry. • Step 2: In applications for children (under the age of 18) the image is standardized and resized before processing. • Step 3: System develops a grid of intersection points from the swirls and arcs of the scanned finger. • Step 4: The image is discarded from the record and is no longer available to the system or any operator. Only a “Template” remains that indicates the intersection points. • Step 5: What MorphoTouch stores and recognizes for each individual is a set of numbers that can only be interpreted as a template. • Comment: The system only remembers and processes numbers for each individual, just like a social security number. The advantages with a biometric approach is that the number cannot be duplicated, lost or stolen, and, uniqueness is defined by the individual.

9. Enrollment and Template Creation • Enrollment • This is the process by which the user’s biometric data is acquired • Templates are created • Presentation • User presents biometric data using hardware such as scanning systems, voice recorders, etc. • Biometric data • Unprocessed image or recording • Feature extraction • Locate and encode distinctive characteristics from biometric data

10. Data Types and Associated Biometric Technologies • Finger scan: Fingerprint Image • Voice scan: Voice recording • Face scan: Facial image • Iris scan: Iris image • Retina scan: Retina image • Hand scan: Image of hand • Signature scan: Image of signature • Keystroke scan: Recording of character types

11. Templates • Templates are NOT compressions of biometric data; they are constructed from distinctive features extracted • Cannot reconstruct the biometric data from templates • Same biometric data supplied by a user at different times may results in different templates • When the biometric algorithm is applied to these templates, it will recognize them as the same biometric data • Templates may consist of strings of characters and numeric values • Vendor systems are heterogeneous; standards are used for common templates and for interoperability

12. Biometric Matching • Part of the Biometric process: Compares the user provided template with the enrolled templates • Scoring: • Each vendor may use a different score for matching; 1-10 or -1 to 1 • Scores also generated during enrollment depending on the quality of the biometric data • User may have to provide different data if enrollment score is low • Threshold is generated by system administrator and varies from system to system and application to application • Decision depending on match/ nomatch • 100% accuracy is generally not possible

13. Metrics for Accuracy in Biometrics Systems • False Match Rate (FMR) • False Nonmatch Rate (FNMR) • Failure to Enroll Rate (FTE) • Derived Metrics

14. False Match Rate • System gives a false positive by matching a user’s biometric with another user’s enrollment • Problem as an imposter can enter the system • Occurs when two people have high degree of similarity • Facial features, shape of face etc. • Template match gives a score that is higher than the threshold • If threshold is increased then false match rate is reduced, but False no match rate is increased • False match rate may be used to eliminate the non-matches and then do further matching

15. False Nonmatch rate • User’s template is matched with the enrolled templates and an incorrect decision of nonmatch is made • Consequence: user is denied entry • False nonmatch occurs for the following reasons • Changes in user’s biometric data • Changes in how a user presents biometric data • Changes in environment in which data is presented • Major focus has been on reducing false match rate and as a result there are higher false nonmatch rates

16. Example Changes in Biometric Data • Finger Scan • Mostly fingerprint remains the same • Facial Scan • Changes in facial hair, weight • Voice scan • Illness can affect voice • Iris Scan • Highly stable • Hand scan • Swelling can change shape

17. Example Changes in Presentation • Different way of presenting enrollment and verification/identification data • Different way of placing fingers and different facial expressions • Volume of speech, change in tone etc. • Changes also depend on the presentation systems used by different vendors

18. Example Changes in Environment • False nonmatch rates can also occur when environment changes even though the biometric data and presentation remain the same • Background lighting, noise in the background, temperature changes etc. • Background noise may affect voice scan and lighting may affect facial scan • Enrollment takes place in a well lit room while verification takes place in a dark room

19. Failure to Enroll Rate • Biometric data for some users may not be clear • E.g., fingerprinting • i.e., users may not have sufficient distinctive biometric data • Enrollment needs • Need high quality enrollment such as two finger scans • Many images for facial scans • Enrollment process varies from vendor to vendor • Examples: • Finger scan: Low quality fingerprints • Facial scan: Poor lighting • Iris scan: glasses

20. Derived Metrics • Derived metrics are obtained by analyzing other metrics such as FMR • Equal error rate ERR • Rate at which FMR is equal to FNMR • Generally such a system is not effective • Ability to verify rate ATV • ATV = (1-FTE)(1-FNMR) • Idea is that if Failure to Enroll is high than False nonmatch rate is also high • More valuable metric

21. Summary • Verification vs Identification • Biometric process • Enrollment and creating templates • Matching templates • Determining if there is a match • Accuracy metrics • False Match • False Nonmatch • Failure to enroll • Biometric systems are not 100% accurate

22. Suggestions for Paper I, Project • Take one Biometric (such as finger scan, face scan) and carry out a survey • Introduction • Algorithms for Face scan and matching • Analysis • Summary and Directions • Biometric Standards, Secure Biometrics, Possibly for Paper II • Feature Extraction Methods • Will have a guest lecture with demonstration on September 12, 2005 • Lei Wang, PhD student of Prof. Latifur Khan