An Automatic Facial Expression Recognition Approach Based on Confusion-Crossed Support Vector Machine Tree

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.