Learning weighted sparse representation of encoded facial normal information for expression-robust 3D face recognition

Handle URI:
http://hdl.handle.net/10754/564443
Title:
Learning weighted sparse representation of encoded facial normal information for expression-robust 3D face recognition
Authors:
Li, Huibin; Di, Huang; Morvan, Jean-Marie; Chen, Liming
Abstract:
This paper proposes a novel approach for 3D face recognition by learning weighted sparse representation of encoded facial normal information. To comprehensively describe 3D facial surface, three components, in X, Y, and Z-plane respectively, of normal vector are encoded locally to their corresponding normal pattern histograms. They are finally fed to a sparse representation classifier enhanced by learning based spatial weights. Experimental results achieved on the FRGC v2.0 database prove that the proposed encoded normal information is much more discriminative than original normal information. Moreover, the patch based weights learned using the FRGC v1.0 and Bosphorus datasets also demonstrate the importance of each facial physical component for 3D face recognition. © 2011 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:
2011 International Joint Conference on Biometrics (IJCB)
Conference/Event name:
2011 International Joint Conference on Biometrics, IJCB 2011
Issue Date:
Oct-2011
DOI:
10.1109/IJCB.2011.6117555
Type:
Conference Paper
ISBN:
9781457713583
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.authorDi, Huangen
dc.contributor.authorMorvan, Jean-Marieen
dc.contributor.authorChen, Limingen
dc.date.accessioned2015-08-04T07:01:07Zen
dc.date.available2015-08-04T07:01:07Zen
dc.date.issued2011-10en
dc.identifier.isbn9781457713583en
dc.identifier.doi10.1109/IJCB.2011.6117555en
dc.identifier.urihttp://hdl.handle.net/10754/564443en
dc.description.abstractThis paper proposes a novel approach for 3D face recognition by learning weighted sparse representation of encoded facial normal information. To comprehensively describe 3D facial surface, three components, in X, Y, and Z-plane respectively, of normal vector are encoded locally to their corresponding normal pattern histograms. They are finally fed to a sparse representation classifier enhanced by learning based spatial weights. Experimental results achieved on the FRGC v2.0 database prove that the proposed encoded normal information is much more discriminative than original normal information. Moreover, the patch based weights learned using the FRGC v1.0 and Bosphorus datasets also demonstrate the importance of each facial physical component for 3D face recognition. © 2011 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleLearning weighted sparse representation of encoded facial normal information for expression-robust 3D face recognitionen
dc.typeConference Paperen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journal2011 International Joint Conference on Biometrics (IJCB)en
dc.conference.date11 October 2011 through 13 October 2011en
dc.conference.name2011 International Joint Conference on Biometrics, IJCB 2011en
dc.conference.locationWashington, DCen
dc.contributor.institutionUniversite de Lyon, CNRS, Franceen
dc.contributor.institutionEcole Centrale de Lyon, URIS UMR5205, F-69134, Lyon, Franceen
dc.contributor.institutionUniversite Lyon 1, Institut Camille Jordan, 43 blvd. du 11 Nov. 1918, F-69622 Villeurbanne-Cedex, Franceen
kaust.authorMorvan, Jean-Marieen
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