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  • Research Article
  • Open Access

A Statistical Multiresolution Approach for Face Recognition Using Structural Hidden Markov Models

EURASIP Journal on Advances in Signal Processing20072008:675787

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

  • Received: 30 April 2007
  • Accepted: 31 October 2007
  • Published:

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.

Keywords

  • Face Recognition
  • Discrete Wavelet Transform
  • Sequential Pattern
  • Discrete Wavelet
  • Conditional Independence

Publisher note

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Authors’ Affiliations

(1)
School of Electronics, Electrical Engineering and Computer Science, Queens University, Belfast, BT7 1NN, UK
(2)
Electrical and Computer Engineering, School of Engineering and Design, Brunel University, London, UB8 3PH, UK
(3)
Department of Mathematics and Computer Science, Grambling State University, Carver Hall, Room 281-C, P.O. Box 1191, LA 71245, USA

Copyright

© P. Nicholl et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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