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

Published in Journal Articles

  1. Mónica Beltrán, Patricia Melin and Leonardo Trujillo. Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration. pages 185–201, Springer Berlin Heidelberg, 2009. URL, DOI BibTeX

    @inbook{Beltrán2009,
    	author = "Beltr{\'a}n, M{\'o}nica and Melin, Patricia and Trujillo, Leonardo",
    	title = "Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration",
    	booktitle = "Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control",
    	year = 2009,
    	publisher = "Springer Berlin Heidelberg",
    	address = "Berlin, Heidelberg",
    	pages = "185--201",
    	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. 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.",
    	isbn = "978-3-642-04514-1",
    	doi = "10.1007/978-3-642-04514-1_10",
    	url = "https://doi.org/10.1007/978-3-642-04514-1_10"
    }
    

Abstract
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.
Published in
Journal of Automation, Mobile Robotics and Intelligent Systems
Volume 4, Issue 1
Pages 185 - 201
http://link.springer.com/chapter/10.1007%2F978-3-642-04514-1_10
Published
October 2009

 

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