Tao, D. and Song, M. and Li, Xuelong and Shen, J. and Sun, J. and Wu, X. and Faloutsos, C. and Maybank, Stephen J. (2008) Bayesian tensor approach for 3-D face modeling. IEEE Transactions on Circuits and Systems for Video Technology 18 (10), pp. 1397-1410. ISSN 1051-8215.
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.
Metadata
| Item Type: | Article |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Administrator |
| Date Deposited: | 07 Feb 2011 13:52 |
| Last Modified: | 09 Aug 2023 12:30 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/1854 |
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