Reference : Deformation Based 3D Facial Expression Representation
Scientific journals : Article
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/34158
Deformation Based 3D Facial Expression Representation
English
Demisse, Girum mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Aouada, Djamila mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
2018
ACM Transactions on Multimedia Computing, Communications, & Applications
Association for Computing Machinery
Yes (verified by ORBilu)
International
1551-6857
[en] 3D facial expression representation ; expression modelling ; 3D face deformation
[en] We propose a deformation based representation for analyzing expressions from 3D faces. A point cloud of a 3D face is decomposed into an ordered deformable set of curves that start from a fixed point. Subsequently, a mapping function is defined to identify the set of curves with an element of a high dimensional matrix Lie group, specifically the direct product of SE(3). Representing 3D faces as an element of a high dimensional Lie group has two main advantages. First, using the group structure, facial expressions can be decoupled from a neutral face. Second, an underlying non-linear facial expression manifold can be captured with the Lie group and mapped to a linear space, Lie algebra of the group. This opens up the possibility of classifying facial expressions with linear models without compromising the underlying manifold. Alternatively, linear combinations of linearised facial expressions can be mapped back from the Lie algebra to the Lie group. The approach is tested on the BU-3DFE and the Bosphorus datasets. The results show that the proposed approach performed comparably, on the BU-3DFE dataset, without using features or extensive landmark points.
http://hdl.handle.net/10993/34158
H2020 ; 689947 - STARR - Decision SupporT and self-mAnagement system for stRoke survivoRs

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