Self-Adaptive Facial Expression Recognition Based on Local Feature Augmentation and Global Information Correlation.

Facial expression recognition(FER) is one of the important research in computer vision, which has been widely applied in human-computer interaction, education, healthcare, transportation, etc. However, the wide application of facial expression recognition technology also brings new challenges, where occlusion and pose variation are two of the worst factors that disturb facial expression recognition in the wild. We propose a facial expression recognition method based on local feature augmentation and multi-scale global correlation which can adaptively extract robust local features and global features from the feature level to suppress the disturbances of occlusion and pose variation on facial expression recognition. The experimental results show that our method performs well on the RAF-DB dataset and has stronger robustness compared with other algorithms.