Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction

Feature transformation and key-point identification is the solution to many local feature descriptors. One among such descriptor is the Scale Invariant Feature Transform (SIFT). A small effort has been made for designing a hexagonal sampled SIFT feature descriptor with its applicability in face reco...

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Detalles Bibliográficos
Autores: Azeem, A., Sharif, M., Shah, J.H., Raza, M.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:México
Institución:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
Repositorio:Journal of Applied Research and Technology
Idioma:inglés
OAI Identifier:oai:ojs2.localhost:article/89
Acceso en línea:https://jart.icat.unam.mx/index.php/jart/article/view/89
Access Level:acceso abierto
Palabra clave:Scale Invariant Feature Transform
Hexagonal image
Resample
Face recognition
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spelling Hexagonal scale invariant feature transform (H-SIFT) for facial feature extractionAzeem, A.Sharif, M.Shah, J.H.Raza, M.Scale Invariant Feature TransformHexagonal imageResampleFace recognitionFeature transformation and key-point identification is the solution to many local feature descriptors. One among such descriptor is the Scale Invariant Feature Transform (SIFT). A small effort has been made for designing a hexagonal sampled SIFT feature descriptor with its applicability in face recognition tasks. Instead of using SIFT on square image coordinates, the proposed work makes use of hexagonal converted image pixels and processing is applied on hexagonal coordinate system. The reason of using the hexagonal image coordinates is that it gives sharp edge response and highlights low contrast regions on the face. This characteristic allows SIFT descriptor to mark distinctive facial features, which were previously discarded by original SIFT descriptor. Furthermore, Fisher Canonical Correlation Analysis based discriminate procedure is outlined to give a more precise classification results. Experiments performed on renowned datasets revealed better performances in terms of feature extraction in robust conditions. All Rights Reserved © 2015 Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0Universidad Nacional Autónoma de México2015-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articleapplication/pdfhttps://jart.icat.unam.mx/index.php/jart/article/view/8910.1016/j.jart.2015.07.006Journal of Applied Research and Technology; Vol. 13 No. 3Journal of Applied Research and Technology; Vol. 13 Núm. 32448-67361665-642310.22201/icat.24486736e.2015.13.3reponame:Journal of Applied Research and Technologyinstname:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICOinstacron:UNAMenghttps://jart.icat.unam.mx/index.php/jart/article/view/89/88Copyright (c) 2018 Journal of Applied Research and Technologyinfo:eu-repo/semantics/openAccessoai:ojs2.localhost:article/892024-08-16T17:54:12Z
dc.title.none.fl_str_mv Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction
title Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction
spellingShingle Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction
Azeem, A.
Scale Invariant Feature Transform
Hexagonal image
Resample
Face recognition
title_short Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction
title_full Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction
title_fullStr Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction
title_full_unstemmed Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction
title_sort Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction
dc.creator.none.fl_str_mv Azeem, A.
Sharif, M.
Shah, J.H.
Raza, M.
author Azeem, A.
author_facet Azeem, A.
Sharif, M.
Shah, J.H.
Raza, M.
author_role author
author2 Sharif, M.
Shah, J.H.
Raza, M.
author2_role author
author
author
dc.subject.none.fl_str_mv Scale Invariant Feature Transform
Hexagonal image
Resample
Face recognition
topic Scale Invariant Feature Transform
Hexagonal image
Resample
Face recognition
description Feature transformation and key-point identification is the solution to many local feature descriptors. One among such descriptor is the Scale Invariant Feature Transform (SIFT). A small effort has been made for designing a hexagonal sampled SIFT feature descriptor with its applicability in face recognition tasks. Instead of using SIFT on square image coordinates, the proposed work makes use of hexagonal converted image pixels and processing is applied on hexagonal coordinate system. The reason of using the hexagonal image coordinates is that it gives sharp edge response and highlights low contrast regions on the face. This characteristic allows SIFT descriptor to mark distinctive facial features, which were previously discarded by original SIFT descriptor. Furthermore, Fisher Canonical Correlation Analysis based discriminate procedure is outlined to give a more precise classification results. Experiments performed on renowned datasets revealed better performances in terms of feature extraction in robust conditions. All Rights Reserved © 2015 Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0
publishDate 2015
dc.date.none.fl_str_mv 2015-06-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://jart.icat.unam.mx/index.php/jart/article/view/89
10.1016/j.jart.2015.07.006
url https://jart.icat.unam.mx/index.php/jart/article/view/89
identifier_str_mv 10.1016/j.jart.2015.07.006
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://jart.icat.unam.mx/index.php/jart/article/view/89/88
dc.rights.none.fl_str_mv Copyright (c) 2018 Journal of Applied Research and Technology
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2018 Journal of Applied Research and Technology
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Nacional Autónoma de México
publisher.none.fl_str_mv Universidad Nacional Autónoma de México
dc.source.none.fl_str_mv Journal of Applied Research and Technology; Vol. 13 No. 3
Journal of Applied Research and Technology; Vol. 13 Núm. 3
2448-6736
1665-6423
10.22201/icat.24486736e.2015.13.3
reponame:Journal of Applied Research and Technology
instname:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
instacron:UNAM
instname_str UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
instacron_str UNAM
institution UNAM
reponame_str Journal of Applied Research and Technology
collection Journal of Applied Research and Technology
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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