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

Biometric Systems Segment Organization Introduction System architecture

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

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

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.

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.

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

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.

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%

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

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

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

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?

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

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?

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?

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

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)

Biometric Systems Segment Organization Introduction System Architecture

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

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)

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.

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.

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

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

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

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?)

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

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

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

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

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