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.
H. Y. Wang and X. Li, “Facial Expression Recognition Based on Improved Gabor Transformation and Adaboost Algorithm,” Journal of Liaoning University of Technology (Natural Science Edition), Vol. 30, No. 1, 2010.
P. S. Aleksic and A. K. Katsaggelos, “Automatic Facial Expression Recognition Using Facial Animation Parameters and Muhistream HMMs,” IEEE Transactions on Information Forensics and Security, Vol. 1, No. 1, 2006, pp. 3-11. doi:10.1109/TIFS.2005.863510