Fingerprint classification based on genetic programming

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@InProceedings{Hu:2010:ICCET,
  author =       "Jiaojiao Hu and Mei Xie",
  title =        "Fingerprint classification based on genetic
                 programming",
  booktitle =    "2nd International Conference on Computer Engineering
                 and Technology (ICCET), 2010",
  year =         "2010",
  month =        "16-18 " # apr,
  volume =       "6",
  pages =        "V6--193--V6--196",
  abstract =     "In this paper, we present a novel algorithm for
                 fingerprint classification. This algorithm classifies a
                 fingerprint image into one of the five classes: Arch,
                 Left loop, Right loop, Whorl, and Tented arch.
                 Initially, preprocessing of fingerprint images is
                 carried out to enhance the image. Then we use genetic
                 programming (GP) to generate new features from the
                 original dataset without prior knowledge. Finally we
                 can classify the fingerprint through a combination of
                 BP network and SVM classifiers, which can not only
                 supplement their advantages, but also improve the
                 computation efficiency. We experiment this algorithm on
                 database from FVC2004. For the five-class problem, a
                 classification accuracy of 93.6percent without any
                 reject, and classification accuracy of 96.2percent with
                 a 15percent reject rate. For the four-class problem
                 (arch and tented arch combined into one class),
                 classification error can be reduced to 3.6percent with
                 only 7.2percent reject rate.",
  keywords =     "genetic algorithms, genetic programming, BP network,
                 FVC2004, SVM classifier, fingerprint classification,
                 four-class problem, image classification,
                 backpropagation, fingerprint identification, image
                 classification, neural nets, support vector machines",
  DOI =          "doi:10.1109/ICCET.2010.5486315",
  notes =        "School of Electronic Engineering, University of
                 Electronic Science and Technology of China Chengdu,
                 China. Also known as \cite{5486315}",
}

Genetic Programming entries for Jiaojiao Hu Mei Xie

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