Local shape spectrum analysis for 3D facial expression recognition
Mostra el registre complet Registre parcial de l'ítem
- dc.contributor.author Derkach, Dmytroca
- dc.contributor.author Sukno, Federico Mateoca
- dc.date.accessioned 2018-04-17T08:58:17Z
- dc.date.available 2018-04-17T08:58:17Z
- dc.date.issued 2017
- dc.description Comunicació presentada a la 12th IEEE International Conference on Automatic Face & Gesture Recognition, celebrada els dies 30 de maig a 3 de juny de 2017 a Washington DC, EUA.
- dc.description.abstract We investigate the problem of facial expression recognition using 3D data. Building from one of the most successful frameworks for facial analysis using exclusively 3D geometry, we extend the analysis from a curve-based representation into a spectral representation, which allows a complete description of the underlying surface that can be further tuned to the desired level of detail. Spectral representations are based on the decomposition of the geometry in its spatial frequency components, much like a Fourier transform, which are related to intrinsic characteristics of the surface. In this work, we propose the use of Graph Laplacian Features (GLF), which results from the projection of local surface patches into a common basis obtained from the Graph Laplacian eigenspace. We test the proposed approach in the BU-3DFE database in terms of expressions and Action Units recognition. Our results confirm that the proposed GLF produces consistently higher recognition rates than the curves-based approach, thanks to a more complete description of the surface, while requiring a lower computational complexity. We also show that the GLF outperform the most popular alternative approach for spectral representation, ShapeDNA, which is based on the Laplace Beltrami Operator and cannot provide a stable basis that guarantee that the extracted signatures for the different patches are directly comparable.en
- dc.description.sponsorship This work is partly supported by the Spanish Ministry of Economy and Competitiveness under the Ramon y Cajal fellowships and the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
- dc.format.mimetype application/pdf
- dc.identifier.citation Derkach D, Sukno FM. Local shape spectrum analysis for 3D facial expression recognition. In: FG 2017. 12th IEEE International Conference on Automatic Face & Gesture Recognition; 2017 May 30-Jun 3; Washington DC, USA. Piscataway (NJ): IEEE; 2017. p. 41-7. DOI: 10.1109/FG.2017.143
- dc.identifier.doi http://dx.doi.org/10.1109/FG.2017.143
- dc.identifier.uri http://hdl.handle.net/10230/34381
- dc.language.iso eng
- dc.publisher Institute of Electrical and Electronics Engineers (IEEE)ca
- dc.relation.ispartof FG 2017. 12th IEEE International Conference on Automatic Face & Gesture Recognition; 2017 May 30-Jun 3; Washington DC, USA. Piscataway (NJ): IEEE; 2017. p. 41-7.
- dc.rights © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published article can be found at http://ieeexplore.ieee.org/document/7961721/
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.subject.keyword Three-dimensional displaysen
- dc.subject.keyword Shapeen
- dc.subject.keyword Laplace equationsen
- dc.subject.keyword Geometryen
- dc.subject.keyword Eigenvalues and eigenfunctionsen
- dc.subject.keyword Faceen
- dc.subject.keyword Two dimensional displaysen
- dc.title Local shape spectrum analysis for 3D facial expression recognitionca
- dc.type info:eu-repo/semantics/conferenceObject
- dc.type.version info:eu-repo/semantics/acceptedVersion