Fingerprint classification based on Adaboost learning from singularity features
Pattern Recognition
r-Theta and orientation invariant transform and signal combining for fingerprint recognition
Expert Systems with Applications: An International Journal
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A biometric encryption approach incorporating fingerprint indexing in key generation
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
A fingerprint retrieval system based on level-1 and level-2 features
Expert Systems with Applications: An International Journal
Evolving estimators of the pointwise Hölder exponent with Genetic Programming
Information Sciences: an International Journal
Fingerprint classification based on decision tree from singular points and orientation field
Expert Systems with Applications: An International Journal
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In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses well defined 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 the NIST-4 database, the correct rates for 4- and 5-class classification are 93.3% and 91.6%, respectively, which compare favorably with other published research and are one of the best results published to date.