中文摘要：

提出一种二维分数阶傅里叶域（2D-FrFT）多阶次特征融合分类算法.该方法充分利用分数阶傅里叶域不同阶次下表情特征之间的相关性,选取两个阶次的表情特征,利用典型相关分析法（Canonical Correlation Analysis,CCA）进行特征融合,并通过基于支持向量机（Support Vector Machine,SVM）的多层次分类机制进行人脸表情识别.仿真实验结果表明,采用多阶次特征融合算法后提高了平均识别率,降低了表情特征维数,减小了计算量.

英文摘要：

In this paper,we explored an approach for recognizing human emotional state from fused 2D-FrFT features based on Canonical Correlation Analysis（CCA）.This approach is mainly based on the correlation between different orders in 2D-FrFT.First,the visual features are extracted by 2D-FrFT,and two orders are choser which achieve the highest recognition rate for feature fusion through CCA.Then we send the fused features into the multi-classifier based on SVM.The feasibility of the recognition approach we proposed has been tested and the experimental results sufficiently demonstrate the effectiveness of the proposed approach.