Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration

  • Mónica Beltrán
  • Patricia Melin
  • Leonardo Trujillo
Part of the Studies in Computational Intelligence book series (SCI, volume 257)


This chapter describes a modular neural network (MNN) with fuzzy integration 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 and a fuzzy inference system. The experimental results obtained using a database of 30 individual’s shows that the modular architecture can achieve a very high 99.33% 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. Furthermore we consider the verification of signatures as false acceptance, false rejection and error recognition of the MNN.


Fuzzy System Discrete Wavelet Transform Fuzzy Controller Fuzzy Inference System Signature Recognition 
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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  1. 1.
    Ballard, D.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)zbMATHCrossRefGoogle Scholar
  2. 2.
    Zhang, D. (ed.): Automated Biometrics Technologies and Systems, ch. 10, Hong Kong Polytechnic University, pp. 203–206. Kluwer Academic Publishers, Dordrecht (2000)Google Scholar
  3. 3.
    Jang, J., Sun, C., Mizutani, E.: Neuro-Fuzzy and Soft Computing, pp. 1–70. Prentice-Hall, Upper Sanddle River (1997)Google Scholar
  4. 4.
    Keller, J., Gader, P., Hocaoglu, A.: Integrals in image processing and recognition. In: Grabisch, M., et al. (eds.) Fuzzy Measures and Integrals: Theory and Applications, pp. 435–466. Physica-Verlag, NY (2000)Google Scholar
  5. 5.
    Grabisch, M.: A new algorithm for identifying fuzzy measures and its application to pattern recognition. In: Proc. of 4th IEEE Int. Conf. on Fuzzy Systems, Yokohama, Japan, pp. 145–150 (1995)Google Scholar
  6. 6.
    Grabisch, M., Murofushi, T., Sugeno, M.: Fuzzy Measures and Integrals: Theory and Applications, pp. 348–373. Physica-Verlag, NY (2000)zbMATHGoogle Scholar
  7. 7.
    Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W.: Hybrid Intelligent Systems Analysis and Design. Springer, Heidelberg (2007)zbMATHCrossRefGoogle Scholar
  8. 8.
    Mendoza, O., Melin, P.: The Fuzzy Sugeno Integral as a Decision Operator in the Recognition of Images with Modular Neural Networks, Tijuana Institute of Technology. Springer, Heidelberg (2007)Google Scholar
  9. 9.
    Melin, P., Mancilla, A., González, C., Bravo, D.: Modular Neuronal Networks with Fuzzy Sugeno Integral Response Integration for Face and Fingerprint Recognition. In: The International Multi Conference in Computer Science and Computer Enginnering, Las Vegas, USA, vol. 1, pp. 91–97 (2004)Google Scholar
  10. 10.
    Melin, P., Mancilla, A., Lopez, M., Solano, D., Soto, M., Castillo, O.: Pattern Recognition for Industrial Security using the Fuzzy Sugeno Integral and Modular Neural Network, Soft Computing in Industrial Applications, vol. 39, pp. 4–9. Springer, Heidelberg (2007)Google Scholar
  11. 11.
    Melin, P., Felix, C., Castillo, O.: Face Recognition using Modular Neural Networks and the Fuzzy Sugeno Integral for Response Integration. Journal of Intelligent Systems 20(2), 29–275 (2005)Google Scholar
  12. 12.
    Melin, P., Gonzalez, C., Bravo, D., Gonzalez, F., Martinez, G.: Modular Neural Networks and Fuzzy Sugeno Integral for Pattern Recognition: Then Case of Human Face and Fingerprint, Tijuana Institute of Technology. Springer, Heidelberg (2007)Google Scholar
  13. 13.
    Melin, P., Castillo, O.: Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems, 1st edn. Studies in Fuzziness and Soft Computing. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  14. 14.
    Melin, P., Castillo, O.: Hybrid Intelligent Systems for Pattern Recognition. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  15. 15.
    Sepulveda, R., Montiel, O., Castillo, O., Melin, P.: Fundamentals of Fuzzy logic, pp. 115–124. ILCSA, ijuana B.C (2002)Google Scholar
  16. 16.
    Kung, S., Mak, M., Lin, S.: Biometric Authentication A Machine Learning Approach. Prentice Hall Information and System Sciences Series, Kailath, T. (series ed.), pp. 27–49 (2005)Google Scholar
  17. 17.
    Santoso, S., Powers, E., Grady, E.: Power quality disturbance data compression using wavelet transform methods. IEEE Trans. on Power Delivery 12(3), 1250–1257 (1997)CrossRefGoogle Scholar

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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