Weighted voting of sparse representation classifiers for facial expression recognition

  title={Weighted voting of sparse representation classifiers for facial expression recognition},
  author={Shane F. Cotter},
  journal={2010 18th European Signal Processing Conference},
  • Shane F. Cotter
  • Published 2010 in 2010 18th European Signal Processing Conference
We present a new algorithm for facial expression recognition that is robust to occlusion. The facial image is divided into equal sized regions, and a Sparse Representation Classifier (SRC) classifies the facial expression in each region. These classification decisions must be combined and different voting methods were considered. A weighted voting method where the vote assigned to each class in a region was based on the class representation error led to the best recognition results under a… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.


Publications referenced by this paper.
Showing 1-10 of 18 references

CVX: MATLAB software for disciplined convex programming (web page and software)”, http://stanford.edu/ boyd/cvx

  • M. Grant, S. Boyd
  • 2009
Highly Influential
3 Excerpts

Ed), “Signal processing on platforms with multiple cores

  • Y. Chen
  • IEEE Sig. Proc. Mag.,
  • 2009
2 Excerpts

Similar Papers

Loading similar papers…