Paper Title:
A Fusion Method of Smile and Laugh Expression Classification
  Abstract

This paper proposed to build a smile expression classification system on data sets of GENKI that can represent real-world environments, and tested its implementation, in which we got the optimal recognition rate up to 86.197%. To deal with the features extraction problems, hybrid features (i.e., Gabor, PHOG, PLBP) are used, using hybrid recognition algorithms (i.e., GentleBoost, SVM) to classify, in this paper. Experiments showed the effectiveness of our methods.

  Info
Periodical
Edited by
Qi Luo
Pages
2364-2369
DOI
10.4028/www.scientific.net/AMM.58-60.2364
Citation
J. Chen, L. H. Guo, Y. Bai, "A Fusion Method of Smile and Laugh Expression Classification", Applied Mechanics and Materials, Vols. 58-60, pp. 2364-2369, 2011
Online since
June 2011
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