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.Full text not available from this repository.
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.
|School or Research Centre:||Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems|
|Date Deposited:||07 Feb 2011 13:52|
|Last Modified:||11 Oct 2016 15:27|
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