DocumentCode :
2603905
Title :
Compound Stochastic Models For Fingerprint Individuality
Author :
Yongfang Zhu ; Dass, Sarat Chandra
Author_Institution :
Dept. of Stat. & Probability, Michigan State Univ., East Lansing, MI
Volume :
3
fYear :
0
fDate :
0-0 0
Firstpage :
532
Lastpage :
535
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 FVC2002 DB1 and IBM HURSLEY databases show that the probability of obtaining a 12 minutiae match purely due to chance is 1.6 times 10-5 when the number of minutiae in the query and template fingerprints are both 46
Keywords :
fingerprint identification; image matching; probability; FVC2002 DB1; IBM HURSLEY database; compound stochastic model; fingerprint database; fingerprint individuality; fingerprint minutiae; minutiae matching; Computer science; Databases; Fingerprint recognition; Fingers; Flowcharts; Nonlinear distortion; Probability; Statistics; Stochastic processes; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
ISSN :
1051-4651
Print_ISBN :
0-7695-2521-0
Type :
conf
DOI :
10.1109/ICPR.2006.397
Filename :
1699581
Link To Document :
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