This paper, we proposed an improved facial expression recognition (FER) method based on region of interesting (ROI) to guide the convolutional neutral networks (CNN) focus on the areas associated with the expression. This method can not only augment the training data, the relationship between the different ROI areas is helpful to intensify the reliability of the predicted targets. In test stage, we investigated two recognition methods: identify the test image directly; implemented decision fusion strategy on ROI areas. The model we used is fine-tuned from pre-trained deep CNN instead of training from scratch. In addition, we presented an innovative region-based image augmentation method named artificial face to increase the limited database. This method using expression retargeting as an expression-preserving data augmentation which is specific for FER. The performance of the proposed method has been validated on the public CK+ databases.