future directions fingerprint

8/14/2019 Future Directions fingerprint

1/24

Fingerprint RecognitionFuture Directions

Salil Prabhakar

Digital Persona Inc.


2/24

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.


3/24

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


4/24

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


5/24

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


6/24

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


7/24

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


8/24


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:


9/24


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


10/24

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.


11/24

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


12/24

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


13/24

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


14/24

Fingerprint RepresentationAccuracy; Representation Limitation

Quality Index = 0.04False Minutiae=27




15/24

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.


16/24

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


17/24

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


18/24

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

=

=


19/24

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)


20/24

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)


21/24

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


22/24

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


23/24

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

future directions fingerprint

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