Fingerprint classification based on learned features

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Tan:2006:tSMC,

• author = "Xuejun Tan and B. Bhanu and Yingqiang Lin",
• title = "Fingerprint classification based on learned features",
• journal = "IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews",
• year = "2005",
• volume = "35",
• number = "3",
• pages = "287--300",
• month = aug,
• keywords = "genetic algorithms, genetic programming, Bayes methods, feature extraction, fingerprint identification, image classification, learning (artificial intelligence), visual databases Bayesian classifier, NIST-4 database, composite operator discovery, feature extraction, feature-learning algorithm, fingerprint classification method, primitive image processing operations",
• DOI = "doi:10.1109/TSMCC.2005.848167",
• ISSN = "1094-6977",
• abstract = "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 93.3percent and 91.6percent, respectively, which compare favourably with other published research and are one of the best results published to date.",

}

Genetic Programming entries for Xuejun Tan Bir Bhanu Yingqiang Lin

Citations