Signature recognition with a hybrid approach combining modular neural networks and fuzzy logic for response integration

• Autorzy: Beltrán M. , Melin P. , Trujillo L. , Lopez M.
• Języki publikacji: EN

Abstrakty

EN

This paper 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.

Słowa kluczowe

EN

pattern recognition; neural networks; fuzzy logic

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2010
• Tom: Vol. 4, No. 1
• Strony: 20--27
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Beltrán M.

autor

Melin P.

autor

Trujillo L.

autor

Lopez M.

• Tijuana Institute of Technology, Tijuana, México,

Bibliografia

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