1. Biometric Systems Adapted from B. Cukic
Biometrics Systems
Adapted from B. Cukic
WVU: Biometric Systems & Information Assurance

2. Biometric Systems Segment Organization
Introduction
System architecture

3. Introduction Biometrics Engineering Definition and Approaches
Definition, Criteria for Selection
Survey of Current Biometrics and Relative Properties
Introduction to socio-legal implications and issues

4. Recap – Identification in the 21st Century
Dispersion of people from their “Natural ID Centers”
Social units have grown to tens of thousands or millions/billions.
Need to assure associations of identity with end-to-end transactions without physical presence
Project your presence (ID) instantly, accurately, and securely across any distance

5. Identification Methods
We need to achieve this recognition automatically in order to authenticate our identity.
Identity is not a passive thing, but associated with an act or intent involving the person with that identity
Seek a manageable engineering definition.

6. Biometric Identification
Pervasive use of biometric ID is enabled by automated systems
Enabled by inexpensive embedded computing and sensing.
Computer controlled acquisition, processing, storage, and matching using biometrics.
Biometric systems are one solution to increasing demand for strong authentication of actions in a global environment.
Biometrics tightly binds an event to an individual
A biometric can not be lost or forgotten, however a biometric must be enrolled.

7. What is an Automated Biometric System?
Biometric Systems
What is an Automated Biometric System?
An automated biometric system uses biological, physiological or behavioral characteristics to automatically authenticate the identity of an individual based on a previous enrollment event.
For the purposes of this course, human identity authentication is the focus. But in general, this need not necessarily be the case.
WVU: Biometric Systems & Information Assurance

8. Characteristics of a Useful Biometric
If a biological, physiological, or behavioral characteristic has the following properties…
Universality
Uniqueness
Permanence
Collectability
….then it can potentially serve as a biometric for a given application.

9. Useful Biometrics 1. Universality
Universality: Every person should possess this characteristic
In practice, this may not be the case
Otherwise, population of nonuniversality must be small < 1%

10. Useful Biometrics 2. Uniqueness
Uniqueness: No two individuals possess the same characteristic.
Genotypical – Genetically linked (e.g. identical twins will have same biometric)
Phenotypical – Non-genetically linked, different perhaps even on same individual
Establishing uniqueness is difficult to prove analytically
May be unique, but “uniqueness” must be distinguishable

11. Useful Biometrics 3. Permanence
Permanence: The characteristic does not change in time, that is, it is time invariant
At best this is an approximation
Degree of permanence has a major impact on the system design and long term operation of biometrics. (e.g. enrollment, adaptive matching design, etc.)
Long vs. short-term stability

12. Useful Biometrics 4. Collectability
Collectability: The characteristic can be quantitatively measured.
In practice, the biometric collection must be:
Non-intrusive
Reliable and robust
Cost effective for a given application

13. Current/Potential Biometrics
Voice
Infrared facial thermography
Fingerprints
Face
Iris
Ear
EKG, EEG
Odor
Gait
Keystroke dynamics
DNA
Signature
Retinal scan
Hand & finger geometry
Subcutaneous blood vessel imaging
What is consensus evaluation of current biometrics based on these four criteria?

14. System-Level Criteria
Our four criteria were for evaluation of the viability of a chosen characteristic for use as a biometric
Once incorporated within a system the following criteria are key to assessment of a given biometric for a specific application:
Performance
User Acceptance
Resistance to Circumvention

15. Central Privacy, Sociological, and Legal Issues/Concerns
System Design and Implementation must adequately address these issues to the satisfaction of the user, the law, and society.
Is the biometric data like personal information (e.g. such as medical information) ?
Can medical information be derived from the biometric data?
Does the biometric system store information enabling a person’s “identity” to be reconstructed or stolen?
Is permission received for any third party use of biometric information?

16. Central Privacy, Sociological, and Legal Issues/Concerns (2)
Continued:
What happens to the biometric data after the intended use is over?
Is the security of the biometric data assured during transmission and storage?
Contrast process of password loss or theft with that of a biometric.
How is a theft detected and “new” biometric recognized?
Notice of Biometric Use. Is the public aware a biometric system is being employed?

17. Biometric System Design
Target Design/Selection of Systems for:
Acceptable overall performance for a given application
Acceptable impact from a socio-legal perspective
Examine the architecture of a biometric system, its subsystems, and their interaction
Develop an understanding of design choices and tradeoffs in existing systems
Build a framework to understand and quantify performance

18. Automated Biometric Identification: A Comprehensive View
Signature
Selection
Iris, Hand, Face, …
Voice, Electro-physiological
Musculo-skeletal,
Molecular, DNA
Microbial …
Biometric
Signature
Acquisition
Camera(s),
Si CMOS System-on-a- chip
Lab on a chip, Implantable med. device…
Data Reduction
Classification
Processing
Minutia extraction
Filtering, FFT, wavelets, Fractals…
Template Storage
Database Search
Match, Retrieval
MATCH?
Identification Process
Identity
Databases,
Time series data
Data Mining
Statistical Modeling…
Arrhythmia,
SIDS,
Biological Agents,
Microbial pathogens...
Action… Logical/Phys. Access (IA, medical, bio)

19. Biometric Systems Segment Organization
Introduction
System Architecture

20. System Architecture Application Authentication Vs. Identification
Enrollment, Verification Modules
Architecture Subsystems

21. Biometric Applications
Four general classes:
Access (Cooperative, known subject)
Logical Access (Access to computer networks, systems, or files)
Physical Access (access to physical places or resources)
Transaction Logging
Surveillance (Non-cooperative, known subject)
Forensics (Non-cooperative or unknown subject)

22. Biometric Applications (2)
Transactions via e-commerce
Search of digital libraries
Computer logins
Access to internet and local networks
Document encryption
Credit cards and ATM cards
Access to office buildings and homes
Protecting personal property
Tracking and storing time and attendance
Law enforcement and prison management
Automated medical diagnostics
Access to medical and official records.

23. System Architecture Architecture Dependent on Application:
Identification: Who are you?
One to Many (millions) match (1:Many)
One to “few” (less than 500) (1:Few)
Cooperative and Non-cooperative subjects
Authentication: Are you who you say you are?
One to One Match (1:1)
Typically assume cooperative subject
Enrollment and Verification Stages common to both.

24. System Architecture (2)
Biometric Systems
System Architecture (2)
Enrollment : Capture and processing of user biometric data for use by system in subsequent authentication operations.
Database
Template
Repository
Acquire and Digitize
Biometric Data
Extract
High Quality Biometric
Features/Representation
Formulate
Biometric
Feature/Rep Template
Authentication/Verification : Capture and processing of user biometric data in order to render an authentication decision based on the outcome of a matching process of the stored to current template.
Acquire and Digitize
Biometric Data
Extract
High Quality Biometric
Features/Representation
Formulate
Biometric
Feature/Rep Template
Template
Matcher
Decision
Output
WVU: Biometric Systems & Information Assurance

25. System Architecture (3)
Authentication Application:
Enrollment Mode/Stage Architecture
Require new acquisition of biometric
Additional image preprocessing, adaptive extraction or representation No
Quality
Sufficient?
Biometric
Data Collection
Signal Processing,
Feature Extraction,
Representation
Transmission
Yes
Database
Approx 512 bytes of data per template
Generate Template

26. System Architecture (4)
Authentication Application:
Verification/Authentication Mode/Stage Architecture No
Require new acquisition of biometric
Additional image preprocessing, adaptive extraction/representation
Quality
Sufficient?
Signal Processing,
Feature Extraction,
Representation
Biometric
Data Collection
Transmission
Yes
Generate Template
Database
Approx 512 bytes of data per template
Template Match
Decision
Confidence? No
Yes

27. Architecture Subsystems
Data Collection
Transmission
Signal Processing/Pattern Matching
Database/Storage
Decision
What comprises these subsystems and how do they interact with other elements (what are their interface and performance specifications?)

28. Architecture Subsystems (2)
Data Collection Module
Biometric choice, presentation of biometric, biometric data collection by sensor and its digitization.
Recollect
Biometric Data Collection
Signal Processing
Feature Extraction
Representation
Transmission
Biometric
Presentation
Sensor

29. Architecture Subsystems (3)
Transmission Module
Compress and encrypt sensor digital data, reverse process.
Recollect
Transmission
Biometric Data Collection
Signal Processing,
Feature Extraction,
Representation
Biometric
Presentation
Sensor
Compression
Encryption
Transmission
Decryption
Decompress

30. Architecture Subsystems (4)
Signal Processing/Matching Module
Be aware of potential transmission prior to match No
Recollect
Reprocess
Transmission
Quality
Control
Signal Processing
Feature Extraction,
Representation
Compression
Encryption
Transmission
Decryption
Decompress
Yes
Generate Template
Database
Template Match
Decision
Confidence? No
Yes

31. Architecture Subsystems
Database module
In what form is biometric stored? Template or raw data? No
Recollect
Reprocess
Transmission
Quality
Control
Signal Processing
Feature Extraction,
Representation
Compression
Encryption
Transmission
Decryption
Expansion
Yes
Generate Template
Database
Templates
Images
Template Match
Biometric Template: A file holding a mathematical representation of the identifying features extracted from the raw biometric data.
Decision
Confidence?
Yes No

32. Architecture Subsystems
Decision module
Is there enough similarity to the stored information to declare a match with a certain confidence ? No
Recollect
Reprocess
Transmission
Quality
Control
Signal Processing
Feature Extraction,
Representation
Compression
Encryption
Transmission
Decryption
Decompress
Yes
Generate Template
Database
Templates
Images
Template Match
Decision
Confidence?
Decision
Confidence?
Yes No