Author(s)

Liying Lang, Zuntao Hu

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In the paper conventional Adaboost algorithm is improved and local features of face such as eyes and mouth are separated as mutual independent elements for facial feature extraction and classification. The multi-expression classification algorithm which is based on Adaboost and mutual independent feature is proposed. In order to effectively and quickly train threshold values of weak classifiers of features, Sample of training is carried out simple improvement. We obtain a good classification results through experiments.

Adaboost Multi-Expression Classification Algorithm, Local Feature, Feature Extraction, Sample Training

L. Lang and Z. Hu, "The Study of Multi-Expression Classification Algorithm Based on Adaboost and Mutual Independent Feature," Journal of Signal and Information Processing, Vol. 2 No. 4, 2011, pp. 270-273. doi: 10.4236/jsip.2011.24038.