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An approach to facial expression recognition integrating radial basis function kernel and multidimensional scaling analysis

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Abstract

To better deal with high dimensions and extract the essential feature of facial expression images in facial expression recognition task, a novel approach integrating radial basis function kernel and multidimensional scaling analysis is proposed in this paper. Firstly, the radial basis function kernel is invoked to map facial expression images to the Hilbert space. Then, Hilbert distance is substituted for the Euclidean distance and a neighbor graph is constructed to express the relationship between data points by employing k nearest neighbor method. Finally, we apply the modified MDS algorithm to reduce the dimension and extract features of facial expression images. Experiments results on the JAFFE database show that this proposed algorithm performs better than Isomap algorithm and supervised Isomap algorithm, and it is more feasible and effective.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (No. 30670576) and Beijing Natural Science Foundation (No. 4122018).

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Authors and Affiliations

  1. School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China

    Shaowei Wang, Zhizheng Zhuo, Hongyu Yang & Haiyun Li

Authors
  1. Shaowei Wang
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  2. Zhizheng Zhuo
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  3. Hongyu Yang
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  4. Haiyun Li
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Corresponding author

Correspondence to Haiyun Li.

Additional information

Communicated by V. Loia.

S. Wang and Z. Zhuo contributed equally to this work.

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Wang, S., Zhuo, Z., Yang, H. et al. An approach to facial expression recognition integrating radial basis function kernel and multidimensional scaling analysis. Soft Comput 18, 1363–1371 (2014). https://doi.org/10.1007/s00500-013-1149-9

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