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EURASIP Journal on Advances in Signal Processing
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A Statistical Multiresolution Approach for Face Recognition Using Structural Hidden Markov Models

EURASIP Journal on Advances in Signal Processing volume 2008, Article number: 675787 (2007) Cite this article

Abstract

This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy.

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Authors and Affiliations

  1. School of Electronics, Electrical Engineering and Computer Science, Queens University, Belfast, BT7 1NN, UK

    P. Nicholl & R. H. Perrott

  2. Electrical and Computer Engineering, School of Engineering and Design, Brunel University, London, UB8 3PH, UK

    A. Amira

  3. Department of Mathematics and Computer Science, Grambling State University, Carver Hall, Room 281-C, P.O. Box 1191, LA, 71245, USA

    D. Bouchaffra

Authors
  1. P. Nicholl
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  2. A. Amira
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  3. D. Bouchaffra
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  4. R. H. Perrott
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Correspondence to P. Nicholl.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Nicholl, P., Amira, A., Bouchaffra, D. et al. A Statistical Multiresolution Approach for Face Recognition Using Structural Hidden Markov Models. EURASIP J. Adv. Signal Process. 2008, 675787 (2007). https://doi.org/10.1155/2008/675787

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  • DOI: https://doi.org/10.1155/2008/675787

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