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An Heuristic Approach for the Training Dataset Selection in Fingerprint Classification Tasks

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Book cover Advances in Neural Networks: Computational and Theoretical Issues

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 37))

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Abstract

Fingerprint classification is a key issue in automatic fingerprint identification systems. It aims to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper an heuristic approach using only the directional image information for the training dataset selection in fingerprint classification tasks is described. The method combines a Fuzzy C-Means clustering method and a Naive Bayes Classifier and it is composed of three modules: the first module builds the working datasets, the second module extracts the training images dataset and, finally, the third module classifies fingerprint images in four classes. Unlike literature approaches using a lot of training examples, the proposed approach requires only 18 directional images per class. Experimental results, conducted on a consistent subset of the free downloadable PolyU database, show a classification rate of 87.59%.

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Author information

Authors and Affiliations

  1. Faculty of Engineering and Architecture, University of Enna Kore, Enna, Italy

    Giuseppe Vitello & Vincenzo Contia

  2. Department of Biopathology, Medical and Forensic Biotechnologies, University of Palermo, Palermo, Italy

    Salvatore Vitabileb

  3. Department of Chemical, Management, Computer and Mechanic Engineering, University of Palermo, Palermo, Italy

    Filippo Sorbello

Authors
  1. Giuseppe Vitello
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  2. Vincenzo Contia
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  3. Salvatore Vitabileb
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  4. Filippo Sorbello
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Corresponding author

Correspondence to Giuseppe Vitello .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Milano, Milano, Italy

    Simone Bassis

  2. Dipartimento di Psicologia & Vietri sul Mare (SA), Seconda Universitá di Napoli, International Institute for Advanced Scientific Studies (IIASS), Caserta, Italy

    Anna Esposito

  3. Department of Civil, Environmental, Energy, and Material Engineering, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

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Vitello, G., Contia, V., Vitabileb, S., Sorbello, F. (2015). An Heuristic Approach for the Training Dataset Selection in Fingerprint Classification Tasks. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-18164-6_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18163-9

  • Online ISBN: 978-3-319-18164-6

  • eBook Packages: EngineeringEngineering (R0)

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