A group of facial normal descriptors for recognizing 3D identical twins

Handle URI:
http://hdl.handle.net/10754/564603
Title:
A group of facial normal descriptors for recognizing 3D identical twins
Authors:
Li, Huibin; Huang, Di; Chen, Liming; Wang, Yunhong; Morvan, Jean-Marie
Abstract:
In this paper, to characterize and distinguish identical twins, three popular texture descriptors: i.e. local binary patterns (LBPs), gabor filters (GFs) and local gabor binary patterns (LGBPs) are employed to encode the normal components (x, y and z) of the 3D facial surfaces of identical twins respectively. A group of facial normal descriptors are thus achieved, including Normal Local Binary Patterns descriptor (N-LBPs), Normal Gabor Filters descriptor (N-GFs) and Normal Local Gabor Binary Patterns descriptor (N-LGBPs). All these normal encoding based descriptors are further fed into sparse representation classifier (SRC) for identification. Experimental results on the 3D TEC database demonstrate that these proposed normal encoding based descriptors are very discriminative and efficient, achieving comparable performance to the best of state-of-the-art algorithms. © 2012 IEEE.
KAUST Department:
Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)
Conference/Event name:
2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012
Issue Date:
Sep-2012
DOI:
10.1109/BTAS.2012.6374588
Type:
Conference Paper
ISBN:
9781467313841
Appears in Collections:
Conference Papers; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLi, Huibinen
dc.contributor.authorHuang, Dien
dc.contributor.authorChen, Limingen
dc.contributor.authorWang, Yunhongen
dc.contributor.authorMorvan, Jean-Marieen
dc.date.accessioned2015-08-04T07:04:55Zen
dc.date.available2015-08-04T07:04:55Zen
dc.date.issued2012-09en
dc.identifier.isbn9781467313841en
dc.identifier.doi10.1109/BTAS.2012.6374588en
dc.identifier.urihttp://hdl.handle.net/10754/564603en
dc.description.abstractIn this paper, to characterize and distinguish identical twins, three popular texture descriptors: i.e. local binary patterns (LBPs), gabor filters (GFs) and local gabor binary patterns (LGBPs) are employed to encode the normal components (x, y and z) of the 3D facial surfaces of identical twins respectively. A group of facial normal descriptors are thus achieved, including Normal Local Binary Patterns descriptor (N-LBPs), Normal Gabor Filters descriptor (N-GFs) and Normal Local Gabor Binary Patterns descriptor (N-LGBPs). All these normal encoding based descriptors are further fed into sparse representation classifier (SRC) for identification. Experimental results on the 3D TEC database demonstrate that these proposed normal encoding based descriptors are very discriminative and efficient, achieving comparable performance to the best of state-of-the-art algorithms. © 2012 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleA group of facial normal descriptors for recognizing 3D identical twinsen
dc.typeConference Paperen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journal2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)en
dc.conference.date23 September 2012 through 27 September 2012en
dc.conference.name2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012en
dc.conference.locationArlington, VAen
dc.contributor.institutionUniversité de Lyon, CNRS, F-69134, Lyon, Franceen
dc.contributor.institutionEcole Centrale Lyon, LIRIS UMR5205, F-69134, Lyon, Franceen
dc.contributor.institutionIRIP, School of Computer Science and Engineering, Beihang University, Beijing, 100191, Chinaen
dc.contributor.institutionUniversité de Lyon, ICJ, 43 blvd du 11 Novembre 1918, F-69622 Villeurbanne-Cedex, Franceen
kaust.authorMorvan, Jean-Marieen
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