Modular Neural Networks with Fuzzy Response Integration for Signature Recognition

Book Chapters

  1. Mónica Beltrán, Patricia Melin and Leonardo Trujillo. Modular Neural Networks with Fuzzy Response Integration for Signature Recognition. In Patricia Melin, Janusz Kacprzyk and Witold Pedrycz (eds.). Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition. Studies in Computational Intelligence series, volume 256, Springer Berlin Heidelberg, 2009, pages 81-91. URL, DOI BibTeX

    @incollection{,
    	year = 2009,
    	isbn = "978-3-642-04515-8",
    	booktitle = "Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition",
    	volume = 256,
    	series = "Studies in Computational Intelligence",
    	editor = "Melin, Patricia and Kacprzyk, Janusz and Pedrycz, Witold",
    	doi = "10.1007/978-3-642-04516-5_5",
    	title = "Modular Neural Networks with Fuzzy Response Integration for Signature Recognition",
    	url = "http://dx.doi.org/10.1007/978-3-642-04516-5_5",
    	publisher = "Springer Berlin Heidelberg",
    	author = "Beltrán, Mónica and Melin, Patricia and Trujillo, Leonardo",
    	pages = "81-91"
    }
    

Abstract

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.

Published in
Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition
Studies in Computational Intelligence
Pages 81-91
Chapter 5
http://link.springer.com/chapter/10.1007%2F978-3-642-04516-5_5
Copyright
2009
ISSN
1860-949X
ISBN
978-3-642-04516-5
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