The Architecture of Biometrics Systems

1. The Architecture of Biometrics Systems Bojan Cukic

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? • 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.

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 • Costeffective for a given application

13. 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 Current/Potential Biometrics • 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. Biometric Signature Selection Biometric Signature Acquisition Data Reduction Classification Processing Minutia extraction Camera(s), Si CMOS System-on-a- chip Iris, Hand,Face, … Voice, Electro-physiological Lab on a chip, Implantable med. device… Filtering, FFT, wavelets, Fractals… Musculo-skeletal, Molecular, DNA Microbial … Automated Biometric Identification: A Comprehensive View 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) 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

25. Approx 512 bytes of data per template 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 Generate Template

26. Approx 512 bytes of data per template 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 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 Yes Compression Encryption Transmission Decryption Decompress 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