Estimating missing tensor data by face synthesis for expression recognition

Huachun Tan; Hao Chen; Jie Zhang

doi:10.1117/12.812973

19 January 2009 Estimating missing tensor data by face synthesis for expression recognition

Huachun Tan, Hao Chen, Jie Zhang

Proceedings Volume 7257, Visual Communications and Image Processing 2009; 72570D (2009) https://doi.org/10.1117/12.812973
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

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.

Citation Download Citation

Huachun Tan, Hao Chen, and Jie Zhang "Estimating missing tensor data by face synthesis for expression recognition", Proc. SPIE 7257, Visual Communications and Image Processing 2009, 72570D (19 January 2009); https://doi.org/10.1117/12.812973

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