intro 2 fpr

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IINTRODUCTIONNTRODUCTION TTOO

FFINGERPRINTINGERPRINT RRECOGNITIONECOGNITION

2001. 2. 232001. 2. 23


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2001/2/23 Introduction to Fingerprint Recognition 2

DefinitionsDefinitions

Biometric System DescriptionBiometric System Description

Fingerprint RecognitionFingerprint Recognition

n Minutiae extractionn Matching

ApplicationsApplications

ProspectiveProspective

ConclusionConclusion

ReferenceReference


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Modern History of FingerprintModern History of Fingerprint

18801880

n Bertillon system (1880):A. Bertillon

n F. Galton Personal ID and Description (1880), Finger Prints(1892) : Minutiae ,(Immutability),

(Individuality)n Vucetich (1891) : Uniqueness

n E. R. Henry (1900) : global structure of fingerprints

Henry System Whorl, Right loop, Left loop, Arch, Tented arch

19501950

n

FBI, NBS(),n FBI, NBS AFIS (1972)

n NEC, Sagem Morpho, Printrack, Cogent : AFIS


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Definition of BiometricsDefinition of Biometrics

Automatic identification or identity verification ofAutomatic identification or identity verification of

livingliving,, humanhuman individuals based onindividuals based on behavioralbehavioraland/orand/orphysiologicalphysiological characteristicscharacteristics

VerificationVerification vv. Identification. Identificationn One-to-one v. One-to-many

Verification can be one-to-many, usually few-to-few

Identification can be one-to-one, usually few-to-some

Fail to account for the reversal in meaning of falseaccept/reject


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Verification (AFAS)Verification (AFAS)

Image AcquisitionDatabase

Feature Extraction

Matching

Decision

ID

DigitizedFingerprint

Features ofSample

Features ofTemplate

Measure ofSimilarity

YES/NO


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Identification (AFIS)Identification (AFIS)

Image Acquisition

DatabaseFeature Extraction

Matching

DigitizedFingerprint

Features of

SampleFeatures of

Templates

Measure of

Similarity

Classification

Fingerprint

Class

Features of Sample

for Classification

List of

Fingerprints andID in the order of

s imilarity


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Positive IdentificationPositive Identification

To prove I am who I say I amTo prove I am who I say I am

Prevent multiple users of a single identityPrevent multiple users of a single identity

Matching sample to single stored templateMatching sample to single stored template

False match allows fraudFalse match allows fraud

False nonFalse non--match causes inconveniencematch causes inconvenience

Multiple alternatives (PIN, ID, etc)Multiple alternatives (PIN, ID, etc)

Can be voluntaryCan be voluntary


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Negative IdentificationNegative Identification

To prove I am not who I say I am notTo prove I am not who I say I am not

Prevent multiple identities of a single userPrevent multiple identities of a single user

Matching sample to all stored templatesMatching sample to all stored templates

False match causes inconvenienceFalse match causes inconvenience

False nonFalse non--match allows fraudmatch allows fraud

No alternativesNo alternatives

Mandatory for all usersMandatory for all users


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Type IType I andand Type IIType II ErrorsErrors

Type I : rejecting a true hypothesisType I : rejecting a true hypothesis

Type II : accepting a false hypothesisType II : accepting a false hypothesis

What is the hypothesis?What is the hypothesis?

Always refer toAlways refer to claim of userclaim of user


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False MatchFalse Match vv..

False NonFalse Non--MatchMatch

Error rates of the matching algorithm fromError rates of the matching algorithm from

a single attempta single attempt--template comparisontemplate comparison

n Impostor : false match

n Genuine : false non-match


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False AcceptanceFalse Acceptance v.v.

False RejectionFalse Rejection

False RejectionFalse Rejection

n Positive ID: failure to acquire or false non-

match after several trials

n Negative ID: failure to acquire or false match

against enrolled template(s)

False AcceptanceFalse Acceptance

n

Negative ID: failure to acquire or false non-match after several trials

n Positive ID: false match against claimed template


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Generic Biometric SystemGeneric Biometric System

DATA

COLLECTION

COMPRESSION EXPANSION

BIOMETRIC

SENSOR

PRESENTATION

SIGNAL

PROCESSING

PATTERN

MATCHING

FEATURE

EXTRACTION

QUALITY

CONTROL

STORAGE

IMAGE STORAGE

DATABASE

DECISION

DECISION

TRANSMISSION

TRANSMISSION


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System DescriptionSystem Description

Data CollectionData Collection

n Biometric Characteristic

n Presentation

wAcceptability : intrusive or non-intrusive ?

n Sensor

wAccessibility : easy to capture by sensor ?

TransmissionTransmission

n Compression/DecompressionwNoise and loss


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Signal ProcessingSignal Processing

n Feature extraction

wRobustness : stable, repeatable, time-invariant ?

wDistinctiveness : variation across the population

n Quality controlwAvailability : independent measures for each user

n Pattern matching

wMatching and Scoring

w Separability : easy to make a decision ?w Possibly multiple matcher


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StorageStorage

n Image storage

w Raw data / Sample data (rarely)

n Database

w Templates / Transaction log

DecisionDecision

n Decision Rules

w Translates scores to decision (reject/accept)

w Thresholding

w Three strike out

w Multiple measures

3k~6kVoice

64+Face

512Iris

14Finger Geometry

9Hand Geometry

200+Fingerprint

Template Sizes (Bytes)


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Score DistributionScore Distribution

IMPOSTER

ProbabilityDistribut

ion

Distance

GENUINE

Genuine

mode 1

Genuine

mode 2

Genuine

mode 3

Imposter

mode 2

Imposter

mode 1

Imposter

mode 3

NEAR FAR

Inter-template curve

InterInter--template curvetemplate curve Imposter curveImposter curve

..


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Advantages v. DisadvantagesAdvantages v. Disadvantages

of Fingerprintof FingerprintAdvantagesAdvantages

n Extremely low false match error rates

n Small and inexpensive sensor size

n Data partitioning through classification

n Some standards

n Forensic acceptability of image

DisadvantagesDisadvantages

n Non-intuitive operation

n Fragility of friction ridges

n No interoperability of standards

n Required sensor cleaning

n Forensic acceptability of image


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Information Level in FingerprintInformation Level in Fingerprint

Level 1Level 1

n Global ridge flow pattern

n Pattern classification

Level 2Level 2

n Local ridge-valley structures

n Minutiae : Ending / Bifurcation

n Singular points : Core / Delta

Level 3Level 3

n Pore structures (1000 dpi)

Duality ofMinutia


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PresentationPresentation

n Inconsistent without user feedback

n Core presentation preferred

n Rotation

n Plastic skin deformation

n Inconsistent contactw Dryness / Moisture

n Irreproducible contact

w Skin damage


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ElectrostaticElectrostatic

SensorsSensors

n Optical

n Capacitive

n Thermal

n Electrostatic

n Acoustic

OpticalOptical

HologramHologram


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No Standard onNo Standard on

Fingerprint ImagesFingerprint Images

Optics

300x300

500dpi

Generated

240x320

500dpi

Semiconduct

or

128x128

250dpi

Optics

288x352

660dpi Ink-rolled512x480

500dpi

Optics

512x480

480dpi


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TransmissionTransmission

CompressionCompression

n Wavelet Scalar Quantization(WSQ) Gray-scale

Fingerprint Image Compression Specification, Criminal

Justice Information Services, FBI, IAFIS-IC-0110v2, Feb

16, 1993.Transmission FormatTransmission Format

n Data Format for the Interchange of Fingerprint

Information, ANSI/NIST-CSL-1-1993.

n Include scar, mark, tattoo in 2000 version.

n Common Biometrics Exchange File Format, v1.0, Feb

2001.


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Signal ProcessingSignal Processing

OpticalOptical CorrelatorCorrelator

Fourier TransformFourier Transform

CorrelationCorrelationMinutia extraction & MatchingMinutia extraction & Matching


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Minutia ExtractionMinutia Extraction

Typical process of minutia extractionTypical process of minutia extraction

Direction Calculation

Minutiae

List

Segmentation

Gray level

Enhancement

Singularity Detection

Binarization

Thinning

Minutiae Detection

Minutiae Heal ing

Matching Module


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MModifiedodified 22DD GaborGabor FilterFilter

OOrientationrientation selectiveselective bandpassbandpass filterfilter

PProductroduct of aof a GaussianGaussian and a sinusoidal waveand a sinusoidal wave

n Sinusoidal wave has a direction and a frequency.

n Gaussian is circular symmetric with a rate of decay.

=

+

+

flowridgetolarperpendicunorientatio:ridgeofwavelength:

Gaussianofvariance:where

k

2

sincos

22

1

2

22

),(

kk yx

j

yx

eeyxG


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Modified 2DModified 2D GaborGabor FilterFilter

Requires local ridge spacing and local ridge orientation.Requires local ridge spacing and local ridge orientation.

n Ridge spacing determines both and .

n Ridge orientation k= tan-1(-kx/ky)

Still difficult for lowStill difficult for low--quality or highquality or high--curvature regioncurvature region


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Direction CalculationDirection Calculation

Direction Field v. Direction imageDirection Field v. Direction image

GaborGabor filter with multiple filter orientationsfilter with multiple filter orientations

n Max magnitude of filter output indicates perpendicular to ridge

direction

Least Square Estimation using GradientLeast Square Estimation using Gradient

( ) ( )

( ) ( ){ }

=

= =

= =

W

i

W

jyx

W

i

W

jyx

o

j,iGj,iG

j,iGj,iG

tan

1 1

22

1 11

2

2

1


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Direction SmoothingDirection Smoothing

Direction flow is smooth and continuous except singular points.Direction flow is smooth and continuous except singular points.


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GrayGray--level Enhancementlevel Enhancement

Histogram equalizationHistogram equalization is not enough.is not enough.

SimpleSimple LP filteringLP filtering reduces noise as well as blurs ridgereduces noise as well as blurs ridge

pattern.pattern.

Orientation selective filteringOrientation selective filtering

n

Non-ridge frequencies are filtered out.

GrayGray--level normalization for quality controllevel normalization for quality control


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SegmentationSegmentation

Discriminating the fingerprint area from the backgroundDiscriminating the fingerprint area from the background

n Background : uniform gray-level without dominant direction

n Fingerprint : large variance in gray-level with direction

MeasuresMeasures

n Variance

n Certainty associated with the direction

n Directional histogram in the block


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Singularity DetectionSingularity Detection

Definition of singular pointsDefinition of singular points

n Core : topmost point on the innermost upward recurving ridge

n Delta : point of flow-bifurcation

PoincarePoincare IndexIndex : integral of the rate of change of: integral of the rate of change of

orientation on a close contourorientation on a close contourn Ordinary points = 0

n Core =

n Delta = -

Arch type do not have any singularity in terms ofArch type do not have any singularity in terms ofPoincarePoincare

indexindex


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BinarizationBinarization ((ThresholdingThresholding) &) &

Thinning (Thinning (SkeletonizingSkeletonizing))Global v. LocalGlobal v. Local thresholdingthresholding

n Histogram of fingerprint image is not bimodal.

n Local thresholding is more adaptive.

w Slit mask perpendicular to ridge direction

w Projection perpendicular to ridge direction

w Zero-crossing ofLaplacian operation

Also requires postAlso requires post--processing to remove holes and islandsprocessing to remove holes and islands

in the binary image.in the binary image.

ThinningThinning

n Make ridge pixels black, valleys white.

n Reduce ridge width to one pixel.


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Minutiae DetectionMinutiae Detection

Possible attributes of minutiae for matchingPossible attributes of minutiae for matching

n Orientation

n Location w.r.t singular points

n Ridge counting along the line from a singular point

n Slope of the line from a singular point

n Minutiae type

( )

( ) ( )

=

=

nBifurcatioat3

Ridgeat2Endingat1

111101111

j,iFj,iI

j,iF


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Heuristic rules for caseHeuristic rules for case--byby--casecase

Possible false minutiaePossible false minutiae

n minutiae too close to each other

n minutiae too close to background

Minutiae HealingMinutiae Healing

Merge Loop Bridge Cross Triangle

Break Spur Ladder DoubleBreak

Break &merge

Island


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Example of Minutia ExtractionExample of Minutia Extraction

Original Result

Binarization

Smoothing

Thinning


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MatchingMatching

What makes it difficult :What makes it difficult :

n Rotation and translation

n Deformation of ridge

n Size of common area

n Repeatability of minutia extraction

Matching processMatching process

n Alignment

n Matching

n Scoring


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Decision PoliciesDecision Policies

Three strikes outThree strikes out

n Systems FNM = FNMFNMFNM

n If errors are independent,

FNMR are NOT independent : A single comparison FNM slightly

increases the probability that a subsequent comparison is FNM.

Above FRR gives the lower bound of FRR.

( ) ( )AFFNMRAFFNMRFRR

AFFNMFRR

FNMRFNM

sys

sys

22

2111

33

3

+=

=

=


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Decision PoliciesDecision Policies

n No systems FM = (No FM) (No FM) (No FM)

n False Accept = No failure to acquire AND system FM

( )

( )[ ] ( )( )AFFMRAFFMRFAR

FMRFMsys

2132111

11

3

3

=

=


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007007Never DieNever Die


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ApplicationsApplications

National ID Card ProgramNational ID Card Programn Korea, Spain, Taiwan, Philippine, Singapore

Crime InvestigationCrime Investigation

n KNPA, FBI's IAFIS and NCIC 2000 Programs

Access ControlAccess Control

n Office, Computer boot-up & logon , Vehicle, Mobile phone, etc.Network Security, eNetwork Security, e--CommerceCommerce

ATM & TeleATM & Tele--Banking (NCR)Banking (NCR)

U.S. Prisons & Border Control (U.S. Prisons & Border Control (DoJDoJ))

Passenger Accelerated Service SystemPassenger Accelerated Service System -- INSPASSINSPASS

n J.F. Kennedy Airport, SF Airport, worldwide.

Welfare BenefitsWelfare Benefits


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Taxonomy of ApplicationsTaxonomy of Applications

Cooperative / NonCooperative / Non--cooperativecooperativen Wolf becomes cooperative in positive ID, but non-cooperative in

negative ID.

Public / PrivatePublic / Privaten Open to public or limited to employees ?

Open / ClosedOpen / Closedn Biometric templates exchangeable to other systems ?

Attended / UnattendedAttended / Unattendedn Supervised or unsupervised ?

Habituated / NonHabituated / Non--habituatedhabituatedn Depending on the frequency of uses


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Taxonomy of ApplicationsTaxonomy of Applications

Overt / CovertOvert / Covertn Users awareness of biometric identifiers being measured

Standard / NonStandard / Non--standard environmentstandard environmentn Operating in controlled indoor or hostile outdoor ?

ExamplesExamplesn INSPASS : cooperative, overt, non-attended, non-habituated, public,

closed, standard environment

n Drivers licensing : non-cooperative, overt, attended, non-habituated,public, open, standard environment

Performance for one environment cannot guarantee the sameperformance for other environment


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Privacy ConcernsPrivacy Concerns

(( ))


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Factors to considerFactors to consider

There are alternatives for positive ID.There are alternatives for positive ID.

Security costs time, money, and effort.Security costs time, money, and effort.

Exception handling is always required.Exception handling is always required.

Testing and evaluation is another technique.Testing and evaluation is another technique.

User acceptance is greater than 90%.User acceptance is greater than 90%.

System integrator makes or breaks the system.System integrator makes or breaks the system.

Beware of orphaned systems.Beware of orphaned systems.

Integrate with current business process.Integrate with current business process.


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ProspectiveProspective

Slow but steady growthSlow but steady growth

Limits on improving error ratesLimits on improving error rates

Great improvement inGreat improvement in human factorhuman factor

MultiMulti--modal biometricsmodal biometrics

Networked biometrics (wired/wireless)Networked biometrics (wired/wireless)Biometrics + SC + PKIBiometrics + SC + PKI

Encrypted biometricsEncrypted biometrics

Unlimited applications for identification andUnlimited applications for identification and

authenticationauthentication


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ConclusionsConclusions

Biometrics has a 120 year history.Biometrics has a 120 year history.

Automation of ID processAutomation of ID process

Positive ID applications are motivated byPositive ID applications are motivated by convenienceconvenience..

Negative ID applications are motivated byNegative ID applications are motivated by necessitynecessity..

Every application requiresEvery application requires customizationcustomization..

One size does not fit all.One size does not fit all.

This isThis is notnot plugplug--andand--playplay..

Successful applications aboundSuccessful applications abound

Integration !Integration !


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ReferencesReferences

J. Wayman, National Biometric Test Center Collected Works, Ver. 1.3,http://www.engr.sjsu.edu/biometrics/nbtccw.pdf, Aug. 2000.

UK Biometrics Working Group, Best practices in testing and reportingperformance of biometric devices, http://www.afb.uk/bwgbestprac10.pdf,Ver. 1.0, Jan. 2000.

A. Jain, et.al., Eds. Biometrics: Information Security in a NetworkedSociety, Kluwer, 1999.

Special Issue on Biometrics, IEEE Computer Magazine, Feb. 2000.

L. Jain, et.al., Eds. Intelligent Biometric Techniques in Fingerprint andFace Recognition, CRC Press, 1999.

A. Jain, et.al., Fingerprint Image Enhancement: Algorithm andPerformance Evaluation, IEEE Trans. On PAMI, Vol.20, No.9, pp.777-789, Aug. 1998.

D. Gabor, Theory of Communication, J. IEE(London), Vol. 93, Part III,No. 26, pp. 429-457, Nov. 1946.

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