ee426 architectures s2003

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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?


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


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

ee426 architectures s2003

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