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Bayesian tensor approach for 3-D face modeling

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.

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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.

Item Type: Article
School or Research Centre: Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems
Depositing User: Administrator
Date Deposited: 07 Feb 2011 13:52
Last Modified: 11 Oct 2016 15:27

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