A statistical multiresolution approach for face recognition using structural hidden Markov models

P. Nicholl, A. Amira, D. Bouchaffra, R. H. Perrott

Research output: Contribution to journalArticle

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. Copyright (c) 2008.
Original languageEnglish
Article number675787
JournalEURASIP Journal on Advances in Signal Processing
DOIs
Publication statusPublished - 2008
Externally publishedYes

Cite this

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title = "A statistical multiresolution approach for face recognition using structural hidden Markov models",
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. Copyright (c) 2008.",
author = "P. Nicholl and A. Amira and D. Bouchaffra and Perrott, {R. H.}",
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language = "English",
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A statistical multiresolution approach for face recognition using structural hidden Markov models. / Nicholl, P.; Amira, A.; Bouchaffra, D.; Perrott, R. H.

In: EURASIP Journal on Advances in Signal Processing, 2008.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A statistical multiresolution approach for face recognition using structural hidden Markov models

AU - Nicholl, P.

AU - Amira, A.

AU - Bouchaffra, D.

AU - Perrott, R. H.

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AB - 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. Copyright (c) 2008.

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