Future Directions fingerprint
Transcript of Future Directions fingerprint
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Fingerprint RecognitionFuture Directions
Salil Prabhakar
Digital Persona Inc.
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Fingerprint Applications
Commercial Government Forensic
Computer Network Logon,
Electronic Data Security,E-Commerce,
Internet Access,
ATM, Credit Card,
Physical Access Control,
Cellular Phones
Personal Digital Assistant,
Medical Records,
Distance Leaning, etc.
National ID card,
Correctional Facilities,Drivers License,
Social Security,
Welfare Disbursement,
Border Control,
Passport Control, etc.
Corpse Identification
Criminal Investigation,Terrorist Identification,
Parenthood determination,
Missing Children, etc.
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Fingerprint Application Functionality
s Positive Identification
Is this person truly know to the system
Commercial applications (network logon)
Desirable: low cost and user-friendly
s Large Scale Identification
Is this person in the database
Government and Forensic applications (prevent double dipping; multiple
passports)
Desirable: high throughput with little human intervention
s Surveillance and Screening
Is this a wanted person
Airport watch list
Fingerprints are not suitable
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Challenges
s
To design a system that would operate on theextremes of all three axis simultaneously
Accuracy
Scale
Usability
101
105
1010
90% 99% 99.9999%
UnusableHard to Use
Easy to use
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Reasons for Accuracy Challenges
s Information Limitation Due to individuality, poor presentation, and inconsistent acquisition
s Representation Limitation Design and choice of representation (features) and quality of feature
extraction algorithms (especially for poor quality fingerprints)
s Invariance Limitation
Incorrect modeling of invariant relationships among features
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Fingerprint Individuality EstimationAccuracy; Information Limitation
s Assumptions for theoretical individuality estimation
consider only minutiae (ending and bifurcation) features
minutiae locations and directions are independent
minutiae locations are uniformly distributed
correspondence of a minutiae pair is an independent event
quality is not explicitly taken into account ridge frequency is assumes to be constant across population and spatially uniform in the
same finger
analysis of matching of different impressions of the same finger binds the parameters of
the probability of matching prints from different fingers
an alignment between two fingerprints has been established
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ro a y o a a se orrespon ence
Accuracy; Information Limitation; Fingerprint Individuality Estimations m = no. of minutiae in template
s n = no. of minutiae in input
s = no. of corresponding minutiae based on location (x,y) alone
s q = no. of corresponding minutiae based on location and direction ( )
s A = area of overlap between input and template
s C = area of tolerance region = r0
2/A
Probability that one of one input minutiae matches any of the m template minutiae:
Probability that two of two input minutiae matches any of the m template minutiae:
A
mC
CA
mCA
A
mCxx2
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ro a y o a a se orrespon ence
Accuracy; Information Limitation; Fingerprint Individuality Estimation
.)1(
))1((...
)1(
)1(
)1(
)1(...
)1(),,,,(
+
+
=
CnA
CnmA
CA
CmA
CA
mCA
CA
Cm
CA
Cm
A
mCnnmCAp
=
CA
mCA
A
mCnnmCAp
1
),,,,(
Probability that 1 of n input minutiae matches any of the m template minutiae:
Probability that q of n input minutiae match any q of the m template minutiae:
C
AMwhere),,,,(
=
n
M
n
mMm
nmCMp
This finally reduces to:
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ro a y o a a se orrespon ence
Accuracy; Information Limitation; Fingerprint Individuality Estimation
( ) ( )C
AMll
qnM
n
mMm
qnmMpnm
q
qq
=
=
where,1),,,(),min(
Finally, since minutiae can lie only on ridges, i.e., along a curve of length A/w,
where w is the ridge-period, M is modified as:
Let l be such that P(min(| i- j|,360-| i- j|) 0) =l. Then,
location.minutiaintolerancelengththeis2where2
/0
0
rr
wAM
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Upper Bound on Fingerprint AccuracyAccuracy; Information Limitation; Fingerprint Individuality Estimation
M,m,n,q P(Correspondence)248, 46, 46, 46 1.33 x 10-77
248, 46, 46, 12 5.86 x 10-7
70, 12, 12, 12 1.22 x 10-20
Database m,n,q P(Correspondence)
MSU_DBI 46, 46, 12 5.8 x 10-2
Theoretical
Empirical
The probabilities of false correspondences for various values of q are computed fromour theoretical model based on the parameters estimated from a Ground Truth database
and the MSU_DBI databases and compared with the empirical probability of false
correspondence obtained from the MSU_DBI database using an automatic fingerprint
matcher.
The entry (70, 12, 12, 12) corresponds to the 12-point guideline.
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Lower Bound on Fingerprint AccuracyAccuracy; Information Limitation; Fingerprint Individuality Estimation
Twin-twin minutiae matching Same-fingerprint-type matching
s Quantify the genetic similarity in fingerprint images
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Information Limitation: ConclusionAccuracy; Information Limitation
s There is an incredible amount of information content in fingerprints
s A minutiae-based fingerprint identification system can distinguish betweenidentical twins
s The performance of state-of-the-art automatic fingerprint matchers do not
even come close to the theoretical performance
s Performance of fingerprint matcher is depended on the fingerprint class
and thus may depend upon target population
s Fingerprint classification may not be very effective in genetically related
population
s Fingerprint identification accuracy may suffer in certain demographics
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Fingerprint RepresentationAccuracy; Representation Limitation
s Ideal representation would maximize the inter-class
variability and minimize the intra-class variability
Fingerprints from the same finger
Minutiae-based representation
may not be most suitable Fingerprints from two different fingers
Ridge feature-based representation
may not be most suitable
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Fingerprint RepresentationAccuracy; Representation Limitation
Quality Index = 0.04False Minutiae=27
Quality Index = 0.53False Minutiae=7
Quality Index = 0.96False Minutiae=0
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Conventional RepresentationsAccuracy; Representation Limitation
s Minutiae-based
Sequential design based on the following modules:
Segmentation, local ridge orientation estimation (singularity and
more detection), local ridge frequency estimation, fingerprint
enhancement, minutiae detection, and minutiae filtering and
post-processing.
s Ridge Feature-based
Size and shape of fingerprint, number, type, and position of
singularities (cores and deltas), spatial relationship and
geometrical attributes of the ridge lines, shape features, globaland local texture information, sweat pores, fractal features.
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Representations: Future DirectionsAccuracy; Representation Limitation
s Improvement of current representations through robust
and reliable domain-specific image processing
techniques such as:
Model-based orientation field estimation
Robust image enhancement and masking
s New richer representations
s Fusion of various representations
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Fingerprint InvarianceAccuracy; Invariance Limitation
s Ideal matcher would perfectly model the invariant
relationship in different impressions of the same
finger
Two good quality fingerprint images from the same finger
A fingerprint matching algorithm that assumes a rigid transformation will be unable to match
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Minutiae MatchingAccuracy; Invariance Limitation
s Given two sets of minutiae points:
s where x, y, and q are the x-coordinate, y-coordinate, and
minutiae direction.
s No point correspondence is known a priori
s
Nonlinear deformation between point sets
s Spurious minutiae and missing minutiae
s Errors in minutiae position and minutiae direction
( ) ( )( )( ) ( )( )QN
Q
N
Q
N
QQQ
P
M
P
M
P
M
PPP
yxyxQ
yxyxP
,,,,,,
,,,,,,
111
111
=
=
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Matching: Future DirectionsAccuracy; Invariance Limitation
s Alignment remains a difficult problem develop
alignment techniques that remain robust under the
presence of false features
s Understand and model fingerprint deformation
s Fusion of various matchers (based on the same or
different representations)
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Scale
s 1:N Identification is a much harder problem (N large)
Accuracy Speed
s Traditionally: classify fingerprint into one of the few (4 or so)
predefined fingerprint types
s Problem: too few distinct bins; uneven natural distribution into
these bins; many ambiguous fingerprints (17% NIST4 has
two labels)
a) b) c)
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Scale: Future Directions
s Continuous classification
s Feature-based indexing (search and retrieval) schemes
(e.g., minutiae triplets)
s Fast matchers
s Classifier combination
0
10
20
30
40
50
60
70
0 5 10 15 20 25 30 35 40
Error (%)
Penet
ration
(%)
minutiae triplets
orientation image
FingerCode
Combination
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Multiple Biometrics; Fusion
s A decision (and lower) level fusion of multiple biometrics can
improve performance
s In identification systems, fusion can also improve speed
s Independence among modalities is key
s Even combination of correlated modalities can be no worse than the
best performing modality alone
s Best combination scheme would be application dependent
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Performance Evaluation
s Evaluation types: technology, scenario, operational
s Dependent on composition of the population(occupation, age, demographics, race), theenvironment, the system operational mode, etc
s Ideally, characterize the application-independent
performance in laboratory and predict technology,scenario, and operational performances
s Standardization and independent testing
s Parametric and non-parametric estimation ofconfidence intervals and database size
s Parametric and non-parametric and statistical modelingof inter-class and intra-class variations;
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Usability, Security, Privacy
s Biometrics are not secrets and not revocable
s Encryption, secure system design, and livenessdetection solve this problem
s Unintended functional scope; unintended application
scope; covert acquisitions Legislation; self-regulation; independent regulatory
organizations
s Biometric Cryptosystems: fingerprint fuzzy vault
Alignment
Similarity metric in encrypted domain
Variable and unordered representation
Performance loss; ROC remains the bottleneck