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

Mónica Beltrán, Patricia Melin () and Leonardo Trujillo
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Mónica Beltrán: Tijuana Institute of Technology
Patricia Melin: Tijuana Institute of Technology
Leonardo Trujillo: Tijuana Institute of Technology

Fuzzy Information and Engineering, 2009, vol. 1, issue 4, 345-355

Abstract: 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.

Keywords: Modular neural networks; Fuzzy integration; Pattern recognition (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/s12543-009-0027-8

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