Human computer interaction technology of expression recognition based on SVM and AdaBoost classifier

This paper is based on the 2DPCA method and introduces a facial expression classification on method which is based on a SVM and AdaBoost algorithm. Firstly, using the method for the face detection of the gray image and obtaining the characteristics data through the wavelet transform and 2DPCA, We reduced the amount of computation effectively. Secondly, we obtain the original classifier by the SVM method to classify learning characteristics data, then through the AdaBoost algorithm to further strengthen the SVM classification results, forming the strong classifier and improving the classification ability. This ensure finishing the work expression recognition and realizing the robustness of the man-machine interaction based on the facial expression recognition of the intelligent wheelchair. The experimental results show that this method not only effectively improves the classification ability of the sample, but also reduces the computational complexity, with an average recognition rate of 92.5% in the intelligent wheelchair human-computer interaction experiment.