EE426 Architectures S2003
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Transcript of EE426 Architectures S2003
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1
The Architecture ofBiometrics Systems
Bojan 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 thousandsor millions/billions.
Need to assure associations of identity withend-to-end transactions withoutphysical
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
automatedsystems Enabled by inexpensive embedded computing and sensing.
Computer controlled acquisition, processing, storage, andmatching using biometrics.
Biometric systems are one solution to increasingdemand for strong authentication of actions in aglobal environment. Biometrics tightly binds an event to an individual
A biometric can not be lost or forgotten, however abiometric 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 authenticatethe identity of an individual based on aprevious enrollment event.
For the purposes of this course, human identityauthentication is the focus. But in general, this neednot necessarily be the case.
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Characteristics of a Useful Biometric
If a biological, physiological, or behavioralcharacteristic 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 thesystem 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
Costeffective 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 vesselimaging
What is consensus evaluation of currentbiometrics 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 agiven 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 thesatisfaction 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 thebiometric data?
Does the biometric system store informationenabling a persons identity to be reconstructed orstolen?
Is permission received for any third party use ofbiometric 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 abiometric.
How is a theft detected and new biometric recognized?
Notice of Biometric Use. Is the public aware abiometric system is being employed?
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Biometric System Design Target Design/Selection of Systems for:
Acceptable overall performance for a givenapplication
Acceptable impact from a socio-legal perspective
Examine the architecture of a biometricsystem, its subsystems, and their interaction
Develop an understanding of design choicesand tradeoffs in existing systems
Build a framework to understand and quantifyperformance
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Automated Biometric Identification: A Comprehensive View
BiometricSignature
Acquisition
Camera(s),
Si CMOSSystem-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
Minutiaextraction
Filtering,FFT,
wavelets,Fractals
Template StorageDatabase Search
Match, Retrieval
Databases,
Time series
dataData Mining
StatisticalModeling
Arrhythmia,
SIDS,
Identity
BiologicalAgents,
Microbialpathogens...
MAT
CH?
ActionLogical/Phys.Access (IA,
medical, bio)
BiometricSignature
Selection
Iris, Hand,Face,
Voice, Electro-physiological
Musculo-skeletal,
Molecular, DNA
Microbial
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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 ApplicationsFour general classes:
Access (Cooperative, known subject)
Logical Access (Access to computer networks, systems, orfiles)
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 toboth.
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System Architecture (2)Enrollment : Capture and processing of user biometricdata for use by system in subsequent authentication
operations.
Acquire and DigitizeBiometric Data
ExtractHigh Quality BiometricFeatures/Representation
FormulateBiometric
Feature/Rep TemplateDatabaseTemplateRepository
Authentication/Verification : Capture and processing of
user biometric data in order to render an authenticationdecision based on the outcome of a matching process ofthe stored to current template.
Acquire and DigitizeBiometric Data
ExtractHigh Quality BiometricFeatures/Representation
FormulateBiometric
Feature/Rep Template
TemplateMatcher
Decision
Output
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System Architecture (3) Authentication Application:
Enrollment Mode/Stage Architecture
Biometric
Data Collection Transmission
Signal Processing,Feature Extraction,
Representation
Quality
Sufficient?
Yes
No
Database Generate Template
Additional image preprocessing,adaptive extraction or
representation
Require new acquisition ofbiometric
Approx 512 bytes ofdata per template
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System Architecture (4) Authentication Application:
Verification/Authentication Mode/Stage Architecture
BiometricData Collection
TransmissionQuality
Sufficient?
Yes
Template Match
Decision
Confidence?
Signal Processing,Feature Extraction,
Representation
No
Database
Generate Template
Additional image preprocessing,adaptive extraction/representation
Require new acquisition ofbiometric
Approx 512 bytes ofdata 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 (whatare their interface and performancespecifications?)
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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
TransmissionBiometric Presentation Sensor
Recollect
Signal ProcessingFeature Extraction
Representation
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Architecture Subsystems (3) Transmission Module
Compress and encrypt sensor digital data, reverseprocess.
Recollect
Biometric Data CollectionTransmission
Biometric Presentation Sensor
Compression
Transmissi
on
Decompre
ss
Encryptio
n
Decryptio
n
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
pression
Transmission
Decompress
Enc
ryption
Dec
ryption
Yes
No
Template MatchDatabase
Generate Template
Reprocess
QualityControl
Recollect
Decision
Confidence? 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
pression
Tran
smission
Exp
ansion
Enc
ryption
Dec
ryption
Yes
No
Template Match
Generate Template
Reprocess
Decision
Confidence?
QualityControl
Recollect
Biometric Template: A fileholding a mathematicalrepresentation of the identifying
features extracted from the rawbiometric data.
DatabaseTemplatesImages
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 ?
Transmission
Signal ProcessingFeature Extraction,
Representation
Com
pression
Tran
smission
Decompress
Encryption
Decryption
Reprocess
Decision
Confidence?
Decision
Confidence?
QualityControl
Recollect
DatabaseTemplatesImages
Template Match
Generate Template
No
No
Yes
Yes