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Artificial neural network approach to authentication of coins by vision-based minimization

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Abstract

A new inspection system, consisting of two procedures for the authentication of coins, is proposed in this paper. In the first procedure, optimum image-matching positions are found by minimizing the matching error of the test coins with their prototype coins. The second procedure is the decision-making process that inspects the coins as genuine or spurious by the Back-Propagation Neural Network combined with the concept of eigen-section. Unlike the traditional approach based on gray-level values, the quantity (8 bits) of the color’s scale has been adopted. The discrimination results are presented and discussed in this study.

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Authors and Affiliations

  1. Department of Marine Engineering, National Taiwan Ocean University, 2 Pei-Ning Road, Keelung, 202-24, Taiwan, ROC

    Jang-Ping Wang, Yi-Cih Jheng & Guo-Ming Huang

  2. Research Centre of Material Processing and Measurement, National Taiwan Ocean University, Keelung, Taiwan, ROC

    Jang-Ping Wang

  3. Asian Coin Certification Authority, 136, Hsi-Ning S.Rd., Taipei, Taiwan, ROC

    Jen-Hsien Chien

Authors
  1. Jang-Ping Wang
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  2. Yi-Cih Jheng
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  3. Guo-Ming Huang
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  4. Jen-Hsien Chien
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Corresponding author

Correspondence to Jang-Ping Wang.

Additional information

This work is one of the projects of coin evaluation supported by National Science council of Taiwan and the company of London Jewelry.

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Wang, JP., Jheng, YC., Huang, GM. et al. Artificial neural network approach to authentication of coins by vision-based minimization. Machine Vision and Applications 22, 87–98 (2011). https://doi.org/10.1007/s00138-009-0197-8

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  • DOI: https://doi.org/10.1007/s00138-009-0197-8

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