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Permutation Coding Technique for Image Recognition System

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Neural Networks and Micromechanics

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Abstract

A feature extractor and neural classifier for a face image recognition system are proposed. They are based on the Permutation Coding technique, which continues our investigation of neural networks. The permutation coding technique makes it possible to take into account not only detected features, but also the position of each feature in the image. It permits us to obtain a sufficiently general description of the image to be recognized. Different types of images were used to test the proposed image recognition system. It was tested on the handwritten digit recognition problem and the face recognition problem. The results of this test are very promising. The error rate for the MNIST database is 0.44%, and for the ORL database it is 0.1%. In the last section, which is devoted to micromechanics applications, we will describe the application of the permutation coding technique to the micro-object shape recognition problem.

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Authors and Affiliations

  1. Center of Applied Research and Technological Development, Autonomous National University of Mexico (UNAM), Mexico, Mexico

    Prof. Ernst Kussul & Prof. Tatiana Baidyk

  2. Dept. of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, USA

    Prof. Donald C. Wunsch II

Authors
  1. Prof. Ernst Kussul
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  2. Prof. Tatiana Baidyk
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  3. Prof. Donald C. Wunsch II
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Correspondence to Ernst Kussul .

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Kussul, E., Baidyk, T., Wunsch, D.C. (2010). Permutation Coding Technique for Image Recognition System. In: Neural Networks and Micromechanics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02535-8_4

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  • DOI: https://doi.org/10.1007/978-3-642-02535-8_4

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