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 l Biometric Terms l Biometric Processes l Accuracy of Biometric Systems

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

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

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

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

8. Biometric Process: Example http://www.foodserve.com/Biometrics%20Defined.pdf l Step 1: Finger is scanned and viewed by the MorphoTouch (Sagem Morpho Inc.) access unit at the point of entry. l Step 2: In applications for children (under the age of 18) the image is standardized and resized before processing. l Step 3: System develops a grid of intersection points from the swirls and arcs of the scanned finger. l 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. l Step 5: What MorphoTouch stores and recognizes for each individual is a set of numbers that can only be interpreted as a template. l 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 l Enrollment - This is the process by which the user’s biometric data is acquired - Templates are created l Presentation - User presents biometric data using hardware such as scanning systems, voice recorders, etc. l Biometric data - Unprocessed image or recording l Feature extraction - Locate and encode distinctive characteristics from biometric data

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

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

12. Biometric Matching l Part of the Biometric process: Compares the user provided template with the enrolled templates l 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 l Threshold is generated by system administrator and varies from system to system and application to application l Decision depending on match/ nomatch - 100% accuracy is generally not possible

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

14. False Match Rate l System gives a false positive by matching a user’s biometric with another user’s enrollment - Problem as an imposter can enter the system l 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 l False match rate may be used to eliminate the non-matches and then do further matching

15. False Nonmatch rate l User’s template is matched with the enrolled templates and an incorrect decision of nonmatch is made l Consequence: user is denied entry l 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 l 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 l Finger Scan - Mostly fingerprint remains the same l Facial Scan - Changes in facial hair, weight l Voice scan - Illness can affect voice l Iris Scan - Highly stable l Hand scan - Swelling can change shape

17. Example Changes in Presentation l 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. l Changes also depend on the presentation systems used by different vendors

18. Example Changes in Environment l False nonmatch rates can also occur when environment changes even though the biometric data and presentation remain the same l 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 l Biometric data for some users may not be clear - E.g., fingerprinting - i.e., users may not have sufficient distinctive biometric data l Enrollment needs - Need high quality enrollment such as two finger scans - Many images for facial scans l Enrollment process varies from vendor to vendor l Examples: - Finger scan: Low quality fingerprints - Facial scan: Poor lighting - Iris scan: glasses

20. Derived Metrics l Derived metrics are obtained by analyzing other metrics such as FMR l Equal error rate ERR - Rate at which FMR is equal to FNMR - Generally such a system is not effective l 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 l Verification vs Identification l Biometric process - Enrollment and creating templates - Matching templates - Determining if there is a match l Accuracy metrics - False Match - False Nonmatch - Failure to enroll l Biometric systems are not 100% accurate

22. Suggestions for Paper I, Project l 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 l Biometric Standards, Secure Biometrics, Possibly for Paper II l Feature Extraction Methods - Will have a guest lecture with demonstration on September 12, 2005 - Lei Wang, PhD student of Prof. Latifur Khan