Compound Stochastic Models For Fingerprint Individuality

Authors:
Yongfang Zhu;Sarat C. Dass;Anil K. Jain
Affiliations:
Michigan State University, East Lansing, MI;Michigan State University, East Lansing, MI;Michigan State University, East Lansing, MI
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

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Abstract

The question of fingerprint individuality can be posed as follows: Given a query fingerprint, what is the probability that the observed number of minutiae matches with a template fingerprint is purely due to chance? An assessment of this probability can be made by estimating the variability inherent in fingerprint minutiae. We develop a compound stochastic model that is able to capture three main sources of minutiae variability in actual fingerprint databases. The compound stochastic models are used to synthesize realizations of minutiae matches from which numerical estimates of fingerprint individuality can be derived. Experiments on the FVC2002DB1 and IBM HURSLEY databases show that the probability of obtaining a 12 minutiae match purely due to chance is 1.6脳10-5 when the number of minutiae in the query and template fingerprints are both 46.