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Face Recognition Method with Two-Dimensional HMM

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Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 403))

Abstract

This paper presents an automatic face recognition system, which bases on two-dimensional hidden Markov models. The traditional HMM uses one-dimensional data vectors, which is a drawback in the case of 2D image processing, as part of the information is lost during conversion. The paper presents the full ergodic 2D-HMM and uses it to identify faces. The experimental results demonstrate that the system, basing on two-dimensional hidden Markov models, is able to achieving an average recognition rate of 94 %.

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Authors and Affiliations

  1. Institute of Computer and Information Science, Czestochowa University of Technology, Dabrowskiego Street 73, 42-200, Czestochowa, Poland

    Janusz Bobulski

Authors
  1. Janusz Bobulski
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Correspondence to Janusz Bobulski .

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Editors and Affiliations

  1. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Robert Burduk

  2. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Konrad Jackowski

  3. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Marek Kurzyński

  4. Dept. of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michał Woźniak

  5. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Andrzej Żołnierek

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Bobulski, J. (2016). Face Recognition Method with Two-Dimensional HMM. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_30

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  • DOI: https://doi.org/10.1007/978-3-319-26227-7_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26225-3

  • Online ISBN: 978-3-319-26227-7

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