Facial Expression Recognition Based on Region Enhanced Attention Network

In order to recognize facial expression images in real environments including complex background, facial occlusion and other factors, a facial expression recognition method based on region enhanced attention network is proposed. Firstly, an attention-based region enhancement network is proposed to reduce the influence of external factors and enhance the robustness of expression recognition in real environments. Then, a channel-spatial attention fusion network is proposed to extract global features. Finally, the recognition degree of facial expression images is improved by the combination of partition loss and cross entropy loss, thereby improving the recognition accuracy. The experimental results on the public datasets RAF-DB, FERPlus and AffectNet show that their expression recognition accuracy is 88.81%, 89.32% and 60.45%. In conclusion, the method in this paper has good accuracy and robustness.