Abstract
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 [59]. 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 [65]. 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.
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© 2012 Springer-Verlag Berlin Heidelberg
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Melin, P. (2012). Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration. In: Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition. Studies in Computational Intelligence, vol 389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24139-0_7
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DOI: https://doi.org/10.1007/978-3-642-24139-0_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24138-3
Online ISBN: 978-3-642-24139-0
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