3D facial expression recognition based on histograms of surface differential quantities

Handle URI:
http://hdl.handle.net/10754/564341
Title:
3D facial expression recognition based on histograms of surface differential quantities
Authors:
Li, Huibin; Morvan, Jean-Marie; Chen, Liming
Abstract:
3D face models accurately capture facial surfaces, making it possible for precise description of facial activities. In this paper, we present a novel mesh-based method for 3D facial expression recognition using two local shape descriptors. To characterize shape information of the local neighborhood of facial landmarks, we calculate the weighted statistical distributions of surface differential quantities, including histogram of mesh gradient (HoG) and histogram of shape index (HoS). Normal cycle theory based curvature estimation method is employed on 3D face models along with the common cubic fitting curvature estimation method for the purpose of comparison. Based on the basic fact that different expressions involve different local shape deformations, the SVM classifier with both linear and RBF kernels outperforms the state of the art results on the subset of the BU-3DFE database with the same experimental setting. © 2011 Springer-Verlag.
KAUST Department:
Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
Springer Science + Business Media
Journal:
Advanced Concepts for Intelligent Vision Systems
Conference/Event name:
13th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2011
Issue Date:
2011
DOI:
10.1007/978-3-642-23687-7_44
Type:
Conference Paper
ISSN:
03029743
ISBN:
9783642236860
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.authorMorvan, Jean-Marieen
dc.contributor.authorChen, Limingen
dc.date.accessioned2015-08-04T06:24:15Zen
dc.date.available2015-08-04T06:24:15Zen
dc.date.issued2011en
dc.identifier.isbn9783642236860en
dc.identifier.issn03029743en
dc.identifier.doi10.1007/978-3-642-23687-7_44en
dc.identifier.urihttp://hdl.handle.net/10754/564341en
dc.description.abstract3D face models accurately capture facial surfaces, making it possible for precise description of facial activities. In this paper, we present a novel mesh-based method for 3D facial expression recognition using two local shape descriptors. To characterize shape information of the local neighborhood of facial landmarks, we calculate the weighted statistical distributions of surface differential quantities, including histogram of mesh gradient (HoG) and histogram of shape index (HoS). Normal cycle theory based curvature estimation method is employed on 3D face models along with the common cubic fitting curvature estimation method for the purpose of comparison. Based on the basic fact that different expressions involve different local shape deformations, the SVM classifier with both linear and RBF kernels outperforms the state of the art results on the subset of the BU-3DFE database with the same experimental setting. © 2011 Springer-Verlag.en
dc.publisherSpringer Science + Business Mediaen
dc.subject3D facial expression recognitionen
dc.subjectcurvature tensoren
dc.subjecthistogram of surface differential quantitiesen
dc.subjectnormal cycle theoryen
dc.subjectSVM classifieren
dc.title3D facial expression recognition based on histograms of surface differential quantitiesen
dc.typeConference Paperen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalAdvanced Concepts for Intelligent Vision Systemsen
dc.conference.date22 August 2011 through 25 August 2011en
dc.conference.name13th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2011en
dc.conference.locationGhenten
dc.contributor.institutionUniversité de Lyon, CNRS, F-69134, Lyon, Franceen
dc.contributor.institutionEcole Centrale de Lyon, LIRIS UMR5205, F-69134, Lyon, Franceen
dc.contributor.institutionUniversité Lyon 1, Institut Camille Jordan, 43 blvd du 11 Novembre 1918, F-69622 Villeurbanne - Cedex, Franceen
kaust.authorMorvan, Jean-Marieen
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