基于改进的Adaboost_SVM的人脸表情识别

针对AdaBoost算法随着学习难度的增加导致分类器的分类效率下降、稳定性变差等问题,支持向量机在小样本中有特有优势;本文结合两种算法优势,基于蚁群算法对SVM的参数进行优化,改进了Adaboost_SVM级联分类算法,首先提取haar-like矩形特征通过Adaboost分类器快速排出非人脸区域;用Gabor小波变换提取人脸表情特征,再结合Adaboost_SVM级联分类器进行人脸表情识别。通过对JAFFE表情库进行试验,表情平均识别率达到94.2%,检测速度有了很大提高。

Facial Expression Recognition Based on Improved Adaboost_SVM Cascade Classifier

Aiming at the fault of reduced classifier efficiency、poor stability and other issues of AdaBoost algo-rithm that caused by the increase of the learning difficulty and the unique advantages that Support vector machine has in small samples, based on ant colony algorithm to optimize the parameters of SVM, this paper improves Ada-boost_SVM cascade classification algorithm combining the advantages of Adaboost algorithm and SVM. Haar-like rectangle features are used to remove non-face by using Adaboost classifier. Gabor wavelet transformation is adopted to extract features of facial expression, and then, combined with Adaboost_SVM cascade classification to recognize facial expression. Experimental result shows that the recognition rate reaches 94.2%and the detect speed has been greatly improved through JAFFE database.