The Biometric Consortium Conference - September 20, 2004 – Arlington, VA 1 Fingerprint Minutiae Attack System Umut Uludag and Prof. Anil K. Jain Computer Science and Engineering Michigan State University [email protected]http://biometrics.cse.msu.edu
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Fingerprint Minutiae Attack System - Danish Biometrics · 14 Minutiae Presence Probabilities Original (histogram-based) smoothed W. ... W ATA. The Biometric Consortium Conference
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The Biometric Consortium Conference - September 20, 2004 – Arlington, VA 1
[Handbook of Fingerprint Recognition, Maltoni et al., 2003]
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Minutiae Presence Probabilities based on Classes• Minutiae can be generated with uniform spatial probability on the 2D grid.
• Inter-ridge distance is 9 pixels, 300x300 target images have 33x33blocks: hence, uniform probability dictates that a minutia can occur in any block with 0.00092 probability.
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Minutiae Presence Probabilities based on Classes
• Experiment:
• NIST 4 database
• Find the image sets for 4 classes: LL, RL, W, T by also including cross-referenced images.
• For each of the 4 classes:
• Find the minutiae locations (r,c) of the fingerprint images.
• Find the core location of the fingerprint images.
• If more than one core is present, pick the uppermost one
• Register images based on core location
• Estimate the spatial probability of minutiae by accumulating the minutiae evidence on a 2D grid, using registered minutiae sets
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Minutiae Presence Probabilities3x3 box filter is used for smoothing the original PDF’ s
smoothedOriginal (histogram-based)LL
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Minutiae Presence Probabilities
smoothedOriginal (histogram-based)RL
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Minutiae Presence Probabilities
Original (histogram-based) smoothed
W
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Minutiae Presence Probabilities
smoothedOriginal (histogram-based)
ATA
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Minutiae Presence Probabilities: 2D images
LL RL
ATAW
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Orientation Fields based on Classes
LL
RL
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Orientation Fields based on Classes
W
ATA
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Experimental Results
• 160 users, 4 impressions/finger, obtained with a VERIDICOM solidstate sensor, 500 dpi, 300x300 images; average # of minutiae is 25
• Operating point of the system: FAR = 0.1%, GAR = 87.6%
FAR & FRR vs. threshold ROC curve
operating point
threshold=12.22
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Sample Images from the Target Database
LL RL
W ATA
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Experimental Results
• FAR=0.1% on the average, 1 in 1,000 imposter attempts will be accepted as a genuine match
• Attacker broke all of the 160 accounts with less than 1,000 attempts for each account
• The minimum, mean, and the maximum number of required attack attempts are: 128, 195, and 488, respectively
• The minimum, mean, and the maximum number of minutiae in the templates that broke the accounts are: 10, 14.2, and 21
• The minimum, mean and the maximum number of matching minutiae between the original template and the templates that broke the accounts are: 5, 6.8, and 10
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Histogram of Number of Attempts
Attempt #: minimum: 128, mean: 195, maximum: 488
(mean of means for 20 different random seed instances: 198)
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Sample account: account# 11
Original image with minutiae
Progression of matching scoresAccount broken at iteration# 192: original template has 16 minutia; synthetic template has 10 minutia; 5 minutiae match; final matching score: 13.3.
Synthetic ( ) and original (o) minutiae
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Evolution of the Synthetic TemplateEvolution of the Synthetic Template
Original image with minutiae
Best initial guess (score: 5.6)
Iteration 192 (score: 13.3)
Iteration 125 (score: 7)
Iteration 150 (score: 8.6)
Iteration 175 (score: 10.5)
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Conclusions
• The proposed attack system is quite effective when breaking the accounts of the large real-world fingerprint database (160 users) considered.
• It needed 195 iterations, on the average, to break all of the accounts.
• Utilization of class priors, minutiae presence probabilities and the class-based orientation fields improved the effectiveness of the attacker greatly.
• The developed minutiae presence probabilities can also be used in fingerprint minutiae individuality studies.