中文摘要：

为了提高人脸表情的正确识别率，提出了一种组合模糊支持向量机（FSVM）K-近邻（KNN）的人脸表情识别的新方法．该方法通过主成分分析（PCA）提取人脸表情特征，对于待分类的不同区域，根据区分程度自适应划分为不同区域类型；并结合FSVM和KNN算法的特点，对不同区域类型切换分类算法．实验表明，此方法既能保证分类的精确度，又能简化计算复杂度．

英文摘要：

To improve the recognition accuracy, a new approach for facial expression recognition based on Fuzzy Support Vector Machine （FSVM） and K-Nearest Neighbor （KNN） is presented in this paper. At first, the feature of the static facial expression image is extracted by the Principle Component Analysis （PCA）, then, the algorithm divide the region into different types, and combine with the characteristic of the FSVM and KNN, switch the classification methods to the different types. The result of the experiment show that proposed algorithm can achieve good recognition accuracy, and can simplify the computation complexity.