Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild

  title={Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild},
  author={Mengyi Liu and Ruiping Wang and Shaoxin Li and Shiguang Shan and Zhiwu Huang and Xilin Chen},
In this paper, we present the method for our submission to the Emotion Recognition in the Wild Challenge (EmotiW 2014). The challenge is to automatically classify the emotions acted by human subjects in video clips under real-world environment. In our method, each video clip can be represented by three types of image set models (i.e. linear subspace, covariance matrix, and Gaussian distribution) respectively, which can all be viewed as points residing on some Riemannian manifolds. Then… CONTINUE READING
Highly Influential
This paper has highly influenced 13 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 132 citations. REVIEW CITATIONS
80 Citations
8 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 80 extracted citations

132 Citations

Citations per Year
Semantic Scholar estimates that this publication has 132 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-8 of 8 references

Caffe: An open source convolutional architecture for fast feature embedding. http://caffe

  • Y. Jia
  • berkeleyvision. org,
  • 2013
Highly Influential
2 Excerpts

Similar Papers

Loading similar papers…