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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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