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8/7/2019 ref paper3
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NIST Fingerprint Image QualityNIST Fingerprint Image Quality
Elham Tabassi
Biometric Consortium Conference
September 20, 2005
8/7/2019 ref paper3
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BCC 2005 [email protected]
backgroundbackgroundo quality is important
o previous research mostly defined as a measure of the extractability of the
features used for recognition such as minutiae.
local orientation information (Bolle et al, Shen et al, Hong etal., )
global features (Hong et al, Lim & Yao, Nill & Bouzas, )
o almost all used subjective quality assessment toevaluate their quality algorithm
size of fingerprint, pressure, humidity, amount of dirt,
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BCC 2005 [email protected]
quality as prediction of performancequality as prediction of performance
we define fingerprint image quality as a prediction of a matcherperformance, e.g. a samples quality score reflects the predictive
positive or negative contribution of an individual sample to the overallperformance of a fingerprint matching system.
FAR
TAR
excellent quality
samples result inhigh performance
poor qualitysamples result inlow performance
8/7/2019 ref paper3
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BCC 2005 [email protected]
use of quality to improve performanceuse of quality to improve performance
o recapture samples of insufficient quality
pruning the poorest quality samples (1.65% of dataset) reduced EERfrom .0047 t0 .0024 (sdkI - dos - ri)
o process samples differently based on their qualities
o collect relevant statistics
compare capture devices and/or environments
correlation among fingers
p(nfiq(ri)=5) = 0.011 p(nfiq(li)=5) = 0.016
p(nfiq(li)=5| nfiq(ri)=5) = 0.22
o cause higher quality sample dominate fusion
8/7/2019 ref paper3
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BCC 2005 [email protected]
qualitynumberNFIQ
NIST Fingerprint Image QualityNIST Fingerprint Image Quality
=1=5
NFIQs 5 levels of quality are intended to be predictive of therelative performance of a minutia based fingerprint matchingsystem.
NFIQ=1 indicates high quality samples, so lower FMR and/orFNMR is expected.
NFIQ=5 indicates poor quality samples, so higher FMR and/orFNMR is expected.
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BCC 2005 [email protected]
performance targetperformance targetdegree of separation between a sampledegree of separation between a samples genuine and imposter distributionss genuine and imposter distributions
4 3 6 6 8 1 0 0 1 3 2 1 6 4 1 9 6 2 2 8
0 .00
0 .05
0 .10
0 .15 m a tch sco res
non m a t ch sc o res
s d 2 9 - v tb m a t c h a n d n o n m a t c h s c o re s h i s to g ra m
nomatch
match
excellent
poor
quality of a biometric sample xi prediction of the bin its normalized
match score falls
8/7/2019 ref paper3
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BCC 2005 [email protected]
pairpair--wise qualitywise quality
fingerprint
matching algorithmsimilarity
score
Q1
Q2
pairwise qualityH(Q1,Q2) QQ1212
when the enrollment sample
is of good quality and better
than that of the use phase(search) sample, the search
samples quality is sufficient
to predict performance.
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BCC 2005 [email protected]
qualitynumberNFIQNFIQ
NIST Fingerprint Image QualityNIST Fingerprint Image Quality
o feature extraction: computes appropriate signal or image fidelitycharacteristics and results in an 11-dimensional feature vector.
o neural network:classifies feature vectors into five classes of qualitybased on various quantiles of the normalized match scoredistribution.
o quality number: an integer value between 1(highest) and 5(poorest).
feature neuralextraction network
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BCC 2005 [email protected]
NFIQ effectivenessNFIQ effectivenesso evaluation criterion is rank ROC as a function of image quality
o used various fingerprint matching algorithms and variousdatasets to evaluate NFIQ
15 different COTS fingerprint matching algorithms
22 different datasets of different fingers captured by various devicesand at different operational settings
each test dataset has 2 fingerprint images of 6000 person
o compared (TAR,FAR) of levels of quality at a fixed threshold
as quality degrades, true accept rate decreases for all the matchers,FAR increase for some.
o levels 2,3,4, and 5 are statistically separable.
o It takes about one third of a second to compute quality of a flatfingerprint image.
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6000 subjects - Right index
Threshold @ (far,tar)=(0.012,0.99)
quality1
excellent2veryGood
3good
4fair
5poor
FAR 0.0037 0.0083 0.0131 0.0216 0.0477
TAR 0.997 0.994 0.993 0.9496 0.926
8/7/2019 ref paper3
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BCC 2005 [email protected]
separable levels of qualityseparable levels of quality
For each quality level,we calculated 95%confidence intervals of
TARs @ FAR=0.1% forsix matchers and sixteendatasets.
nfiq levels 2,3,4, and 5are statistically separate.
8/7/2019 ref paper3
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BCC 2005 [email protected]
conclusionconclusion
o a novel definition of fingerprint image qualityo NFIQ works as a rank statistic for performance for all 330
combinations of COTS fingerprint matchers andoperational datasets tested
o NFIQ levels 2,3,4, and 5 are statistically independento NFIQ can be used for real-time quality assessment
o NFIQ is publicly available but subject to US export control
laws (fingerprint.nist.gov)
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thanksthanks
[email protected]@nist.gov
301 975 5292301 975 5292
mailto:[email protected]:[email protected]:[email protected]