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.