Modular Neural Networks with Fuzzy Response Integration for Signature Recognition

  • Mónica Beltrán
  • Patricia Melin
  • Leonardo Trujillo
Part of the Studies in Computational Intelligence book series (SCI, volume 256)


This chapter 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 a 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 using a Sugeno fuzzy integral. The experimental results obtained 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.


Discrete Wavelet Transform Wavelet Coefficient Signature Recognition Feature Extraction Method Fuzzy Measure 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mónica Beltrán
    • 1
  • Patricia Melin
    • 1
  • Leonardo Trujillo
    • 1
  1. 1.Tijuana Institute of TechnologyTijuanaMéxico

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