面部表情自动分类是情感信息处理研究中的重要内容，为了提高表情识别的准确率以及鲁棒性，提出了一种基于混淆交叉支撑向量机树的面部表情自动分类方法。该方法依据伪Zernike矩特征，以混淆交叉支撑向量机树对矩特征进行学习，实现面部表情的自动分类。混淆交叉支撑向量机树的结构使模型能够根据教师信号将面部表情识别问题分解，在不同的层次上以相对较低的复杂度解决子问题；在训练阶段，对当前中间节点划分的两个子样本集进行混淆交叉，增强了模型在面部表情识别上的整体泛化性能以及鲁棒性。实验对Cohn Kanade面部表情数据库中的6类基本表情进行自动分类，准确率达到96.31%；与同样基于该数据库的识别方法相比，该方法在识别正确率和鲁棒性上具有较大的优势。

Automatic facial expression recognition is the kernel part of emotional information processing. This study is dedicated to develop an automatic facial expression recognition approach based on confusion crossed support vector machine tree (CSVMT) to improve recognition accuracy and robustness. Pseudo Zernike moment features were extracted to train a CSVMT for automatic recognition. The structure of CSVMT enables the model to divide the facial recognition problem into sub problems according to the teacher signals, so that it can solve the sub problems in decreased complexity in different tree levels. In the training phase, those sub samples assigned to two internal sibling nodes perform decreasing confusion cross, thus, the generalization ability of CSVMT for recognition of facial expression is enhanced. The experiments are conducted on Cohn Kanade facial expression database. Competitive recognition accuracy 9631% is achieved. The compared results on Cohn Kanade facial expression database also show that the proposed approach appeared higher recognition accuracy and robustness than other approaches.