基于表情子空间多分类器集成的非特定人人脸表情识别

针对非特定人人脸表情平均识别率普遍不高（约65%）的问题，提出了一种基于表情子空间和多分类器集成的人脸表情识别新方法。通过局部二进制模式(LBP)与高阶奇异值分解(HOSVD)方法对训练集1中的人脸图像的全脸、眼睛（包括眉毛）和嘴巴三个区域进行特征提取与分解，建立相应的表情子空间；利用支持向量机(SVM)方法对训练集2中的人脸图像在表情子空间训练，得到模糊系统参数；最后结合表情子空间与多分类器集成，对测试集中的图像进行表情分类识别。在JAFFE人脸表情库中实验，获得了71.43%的平均识别率。实验结果表明，该方法有效地减少了人脸外观特征和表情表现方式所带来的影响，具有更好的识别效果。

Person-independent facial expression recognition based on expression subspace multi-classifiers integration

To the problem that the average recognition rate of person-independent facial expression is not high (about 65%), a new method of facial expression recognition, based on expression subspace and multi-classifiers integration, was proposed. In the training set 1, the features of global face region, eyes (include eyebrows) region and mouth region were respectively extracted and decomposed by Local Binary Pattern (LBP) and Higher Order Singular Value Decomposition (HOSVD), and the corresponding expression subspaces were built. Then the facial images of the training set 2 were trained by Support Vector Machine (SVM) in the expression subspaces and the parameters of fuzzy rule system were conducted. Finally, the expression subspaces and the multi-classifiers ensemble were combined to classify the expressions in test set. The experiments were conducted on JAFFE database and the average recognition rate was 71.43%. The experimental results show that the proposed method effectively reduces the influence caused by facial shape feature and facial expression manner, and it has better recognition rate.