Introduction to Biometrics

Dr. Bhavani Thuraisingham

The University of Texas at Dallas

Lecture #5

Issues on Designing Biometric Systems

September 7, 2005

Outline

Biometric Terms Biometric Processes Accuracy of Biometric Systems

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

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

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

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

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

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.

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

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

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

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

Metrics for Accuracy in Biometrics Systems

False Match Rate (FMR) False Nonmatch Rate (FNMR) Failure to Enroll Rate (FTE) Derived Metrics

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

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

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

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

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

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

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

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

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