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Proceedings Paper

Estimating missing tensor data by face synthesis for expression recognition
Author(s): Huachun Tan; Hao Chen; Jie Zhang
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Paper Abstract

In this paper, a new method of facial expression recognition is proposed for missing tensor data. In this method, the missing tensor data is estimated by facial expression synthesis in order to construct the full tensor, which is used for multi-factorization face analysis. The full tensor data allows for the full use of the information of a given database, and hence improves the performance of face analysis. Compared with EM algorithm for missing data estimation, the proposed method avoids iteration process and reduces the estimation complexity. The proposed missing tensor data estimation is applied for expression recognition. The experimental results show that the proposed method is performing better than only utilize the original smaller tensor.

Paper Details

Date Published: 19 January 2009
PDF: 6 pages
Proc. SPIE 7257, Visual Communications and Image Processing 2009, 72570D (19 January 2009); doi: 10.1117/12.812973
Show Author Affiliations
Huachun Tan, Beijing Institute of Technology (China)
Hao Chen, Beijing Institute of Technology (China)
Jie Zhang, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 7257:
Visual Communications and Image Processing 2009
Majid Rabbani; Robert L. Stevenson, Editor(s)

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