An Automatic Facial Expression Recognition Approach Based on Confusion-Crossed Support Vector Machine Tree
Qinzhen Xu, Pinzheng Zhang, Wenjiang Pei, Lüxi Yang, Zhenya He · 2007
Automatic facial expression recognition is the kernel part of emotional information processing. This paper dedicates to develop an automatic facial expression recognition approach based on confusion-crossed support vector machine tree (CSVMT) to improve recognition accuracy and robustness. After the pseudo-Zernike moment features were extracted, they were used 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 compared results on Cohn-Kanade facial expression database also show that the proposed approach appeared higher recognition accuracy and robustness than other approaches.