A Fusion Method of Smile and Laugh Expression Classification

Abstract:

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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 et al., "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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Price:

$35.00

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