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IINTRODUCTIONNTRODUCTION TTOO
FFINGERPRINTINGERPRINT RRECOGNITIONECOGNITION
2001. 2. 232001. 2. 23
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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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System DescriptionSystem Description
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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System DescriptionSystem Description
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.