Post on 21-Dec-2015
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Biometrics Systems
Adapted from B. Cukic
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Biometric Systems Segment Organization Introduction System architecture
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Biometrics Engineering Definition and Approaches Definition, Criteria for Selection Survey of Current Biometrics and Relative Properties Introduction to socio-legal implications and issues
Introduction
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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
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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.
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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.
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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.
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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.
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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%
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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
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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
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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
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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?
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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
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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?
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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?
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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
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Automated Biometric Identification: A Comprehensive Automated Biometric Identification: A Comprehensive ViewView
BiometricSignature
Acquisition
Camera(s),
Si CMOS System-on-
a- chip
Lab on a chip, Implantable
med. device…
Data ReductionClassification
Processing
0.0 0.5 1.0 1.5 2.0 2.5
Minutia extractio
n
Filtering, FFT,
wavelets,
Fractals…
Template StorageDatabase SearchMatch, Retrieval
Databases,
Time series data
Data Mining
Statistical Modeling…
Arrhythmia,
SIDS,
Identity
Biological Agents,
Microbial pathogens..
.
MATCH?
Action… Logical/Phys. Access (IA,
medical, bio)
Biometric SignatureSelection
Iris, Hand, Face, …
Voice, Electro-
physiological
Musculo-skeletal,
Molecular, DNA
Microbial …
Identification Process
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Biometric Systems Segment Organization Introduction System Architecture
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System Architecture
Application Authentication Vs. Identification Enrollment, Verification Modules Architecture Subsystems
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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)
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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.
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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.
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System Architecture (2)Enrollment : Capture and processing of user biometric data for use by system in subsequent authentication operations.
Acquire and DigitizeBiometric Data
ExtractHigh Quality Biometric
Features/Representation
Formulate Biometric
Feature/Rep TemplateDatabaseTemplate Repository
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 DigitizeBiometric Data
ExtractHigh Quality Biometric
Features/Representation
Formulate Biometric
Feature/Rep Template
Template Matcher
Decision
Output
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System Architecture (3) Authentication Application:
Enrollment Mode/Stage Architecture
BiometricData Collection Transmission
Signal Processing, Feature Extraction,
Representation
Quality Sufficient?
Yes
No
Database Generate Template
Additional image preprocessing, adaptive
extraction or representation
Require new acquisition of biometric
Approx 512 bytes of data per template
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System Architecture (4) Authentication Application:
Verification/Authentication Mode/Stage Architecture
BiometricData Collection
TransmissionQuality
Sufficient?
Yes
Template Match
DecisionConfidence?
Signal Processing, Feature Extraction,
Representation
No
Database
Generate Template
Additional image preprocessing, adaptive
extraction/representation
Require new acquisition of biometric
Approx 512 bytes of data per template
NoYes
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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?)
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Architecture Subsystems (2) Data Collection Module
Biometric choice, presentation of biometric, biometric data collection by sensor and its digitization.
Biometric Data Collection
TransmissionBiometricPresentation Sensor
Recollect
Signal Processing Feature Extraction
Representation
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Architecture Subsystems (3) Transmission Module
Compress and encrypt sensor digital data, reverse process.
Recollect
Biometric Data Collection Transmission
BiometricPresentation Sensor
Com
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Tra
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Deco
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Encr
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Decr
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Signal Processing,Feature Extraction,
Representation
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Architecture Subsystems (4) Signal Processing/Matching Module
Be aware of potential transmission prior to match
TransmissionSignal Processing
Feature Extraction,Representation
Com
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Tra
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Deco
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Encr
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Decr
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Yes
No
Template MatchDatabase
Generate Template
Reprocess
Quality Control
Recollect
DecisionConfidence?
NoYes
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Architecture Subsystems Database module
In what form is biometric stored? Template or raw data?
TransmissionSignal Processing
Feature Extraction,Representation
Com
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Tra
nsm
issi
on
Expansi
on
Encr
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Decr
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Yes
No
Template Match
Generate Template
Reprocess
DecisionConfidence?
Quality Control
Recollect
Biometric Template: A file holding a mathematical representation of the identifying features extracted from the raw biometric data.
DatabaseTemplates
Images
NoYes
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Architecture Subsystems Decision module
Is there enough similarity to the stored information to declare a match with a certain confidence ?
TransmissionSignal Processing
Feature Extraction,Representation
Com
pres
sion
Tran
smiss
ion
Deco
mpr
ess
Encr
yptio
n
Decr
yptio
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Reprocess
DecisionConfidence?Decision
Confidence?
Quality Control
Recollect
DatabaseTemplates
Images
Template Match
Generate Template
No
No
Yes
Yes