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Nature-Inspired Design of Hybrid Intelligent Systems pp 3–18Cite as

General Type-2 Fuzzy Edge Detection in the Preprocessing of a Face Recognition System

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 667))

Abstract

In this paper, we present the advantage of using a general type-2 fuzzy edge detector method in the preprocessing phase of a face recognition system. The Sobel and Prewitt edge detectors combined with GT2 FSs are considered in this work. In our approach, the main idea is to apply a general type-2 fuzzy edge detector on two image databases to reduce the size of the dataset to be processed in a face recognition system. The recognition rate is compared using different edge detectors including the fuzzy edge detectors (type-1 and interval type-2 FS) and the traditional Prewitt and Sobel operators.

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Acknowledgment

We thank the MyDCI program of the Division of Graduate Studies and Research, UABC, and the financial support provided by our sponsor CONACYT contract grant number: 44524.

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Authors and Affiliations

  1. Autonomous University of Baja California, Tijuana, Mexico

    Claudia I. Gonzalez, Juan R. Castro & Olivia Mendoza

  2. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin & Oscar Castillo

Authors
  1. Claudia I. Gonzalez
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  2. Patricia Melin
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  3. Juan R. Castro
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  4. Olivia Mendoza
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  5. Oscar Castillo
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Corresponding author

Correspondence to Oscar Castillo .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Oscar Castillo

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Gonzalez, C.I., Melin, P., Castro, J.R., Mendoza, O., Castillo, O. (2017). General Type-2 Fuzzy Edge Detection in the Preprocessing of a Face Recognition System. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-47054-2_1

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