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Fingerprint classification
based on learned features
Presented by: Xuejun Tan
Abstract:
A fingerprint classification approach based on a novel feature-learning
algorithm. Unlike current research for fingerprint classification
that generally uses visually meaningful features, our approach
is based on Genetic Programming (GP), which learns to discover
composite operators and features that are evolved from combinations
of primitive image processing operations. Our experimental
results show that our approach can find good composite operators
to effectively extract useful features. Using a Bayesian classifier,
without rejecting any fingerprints from NIST-4 database, the
correct rates for 4 and 5-class classification are 93.3% and
91.2% respectively, which compare favorably and have advantages
over the best results published to date.
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