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dc.contributor.authorAsif, M Daud Abdullah
dc.contributor.authorGao, Yongsheng
dc.contributor.authorZhou, Jun
dc.contributor.editorMurshed, M
dc.contributor.editorPaul, M
dc.contributor.editorAsikuzzaman, M
dc.contributor.editorPickering, M
dc.contributor.editorNatu, A
dc.contributor.editorRoblesKelly, A
dc.contributor.editorYou, S
dc.contributor.editorZheng, L
dc.contributor.editorRahman, A
dc.date.accessioned2019-07-04T12:37:43Z
dc.date.available2019-07-04T12:37:43Z
dc.date.issued2018
dc.identifier.isbn978-1-5386-6602-9
dc.identifier.doi10.1109/dicta.2018.8615831
dc.identifier.urihttp://hdl.handle.net/10072/384217
dc.description.abstractIn this paper, we propose a novel Local Mesh High-order Pattern Descriptor (LMHPD) for face recognition. This description is constructed in a high-order derivative space and is integrated with a Convolutional Neural Network (CNN) architecture. Based on the information collected at a local neighborhood of reference pixel with diverse radiuses and mesh angles, a vectorized feature representation of the reference pixel is generated to provide micro-patterns. They are then converted to multi-channels to use in conjunction with the CNN. The CNN adopted in the proposed architecture is generic and very compact with a small number of convolutional layers. However, LMHPD is derived in such a way that it can work with most of the available CNN architectures. For keeping the computational cost and time complexity at the minimum, we propose a lighter approach of high-order texture descriptor with CNN architecture that can effectively extract discriminative face features. Extensive experiments on Extended Yale B and CMU-PIE datasets show that our method consistently outperforms several alternative descriptors for face recognition under various circumstances.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.relation.ispartofconferencename2018 Digital Image Computing: Techniques and Applications (DICTA)
dc.relation.ispartofconferencetitle2018 Digital Image Computing: Techniques and Applications (DICTA)
dc.relation.ispartofdatefrom2018-12-10
dc.relation.ispartofdateto2018-12-13
dc.relation.ispartoflocationCanberra, Australia
dc.relation.ispartofpagefrom1
dc.relation.ispartofpagefrom7 pages
dc.relation.ispartofpageto7
dc.relation.ispartofpageto7 pages
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleFace Recognition with Multi-channel Local Mesh High-order Pattern Descriptor and Convolutional Neural Network
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorZhou, Jun
gro.griffith.authorGao, Yongsheng


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