Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/10653
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dc.contributor.authorNicholl, P-
dc.contributor.authorAmira, A-
dc.contributor.authorBouchaffra, D-
dc.contributor.authorPerrott, RH-
dc.date.accessioned2015-04-24T13:38:21Z-
dc.date.available2008-
dc.date.available2015-04-24T13:38:21Z-
dc.date.issued2007-
dc.identifier.citationEURASIP Journal on Advances in Signal Processing, 2008: 675787en_US
dc.identifier.issn675787-
dc.identifier.issn675787-
dc.identifier.issn1687-6172-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/10653-
dc.description.abstractThis 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.en_US
dc.languageEN-
dc.language.isoenen_US
dc.publisherHindawi Publishing Corporationen_US
dc.subjectRepresentationen_US
dc.subjectAlgorithmsen_US
dc.subjectPCAen_US
dc.titleA statistical multiresolution approach for face recognition using structural hidden Markov modelsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1155/2008/675787-
dc.relation.isPartOfEURASIP Journal on Advances in Signal Processing-
dc.relation.isPartOfEURASIP Journal on Advances in Signal Processing-
pubs.volume2008-
pubs.volume2008-
pubs.organisational-data/Brunel-
pubs.organisational-data/Brunel/Leavers-
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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