Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2008

Abstract

Effectively modeling a collection of three-dimensional (3-D) faces is an important task in various applications, especially facial expression-driven ones, e.g., expression generation, retargeting, and synthesis. These 3-D faces naturally form a set of second-order tensors-one modality for identity and the other for expression. The number of these second-order tensors is three times of that of the vertices for 3-D face modeling. As for algorithms, Bayesian data modeling, which is a natural data analysis tool, has been widely applied with great success; however, it works only for vector data. Therefore, there is a gap between tensor-based representation and vector-based data analysis tools. Aiming at bridging this gap and generalizing conventional statistical tools over tensors, this paper proposes a decoupled probabilistic algorithm, which is named Bayesian tensor analysis (BTA). Theoretically, BTA can automatically and suitably determine dimensionality for different modalities of tensor data. With BTA, a collection of 3-D faces can be well modeled. Empirical studies on expression retargeting also justify the advantages of BTA.

Keywords

3-D face, Bayesian inference, Bayesian tensor analysis, face expression synthesis, face recognition

Discipline

Databases and Information Systems

Publication

IEEE Transactions on Circuits and Systems for Video Technology

Volume

18

Issue

10

First Page

1397

Last Page

1410

ISSN

1051-8215

Identifier

10.1109/TCSVT.2008.2002825

Publisher

IEEE

Copyright Owner and License

Authors

Additional URL

http://doi.org/10.1109/TCSVT.2008.2002825

Download

Share

COinS