Fingerprint classification based on learned features

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  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