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Development of modular neural networks with fuzzy logic response integration for signature recognition

Author

Listed:
  • Mónica Beltrán

    (Tijuana Institute of Technology)

  • Patricia Melin

    (Tijuana Institute of Technology)

  • Leonardo Trujillo

    (Tijuana Institute of Technology)

Abstract

This paper describes a modular neural network (MNN) for the problem of signature recognition. Currently, biometric identification has gained a great deal of research interest within the pattern recognition community. For instance, many attempts have been made in order to automate the process of identifying a person’s handwritten signature, however this problem has proven to be a very difficult task. In this work, we propose an MNN that has three separate modules, each using different image features as input, these are: edges, wavelet coefficients, and the Hough transform matrix. Then, the outputs from each of these modules are combined by using a Sugeno fuzzy integral. The experimental results obtained by using a database of 30 individual’s shows that the modular architecture can achieve a very high 98% recognition accuracy with a test set of 150 images. Therefore, we conclude that the proposed architecture provides a suitable platform to build a signature recognition system.

Suggested Citation

  • Mónica Beltrán & Patricia Melin & Leonardo Trujillo, 2009. "Development of modular neural networks with fuzzy logic response integration for signature recognition," Fuzzy Information and Engineering, Springer, vol. 1(4), pages 345-355, December.
    • Handle: RePEc:spr:fuzinf:v:1:y:2009:i:4:d:10.1007_s12543-009-0027-8
    DOI: 10.1007/s12543-009-0027-8
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