Since the labeled wild facial expression database is relatively rare, the existing Facial Expression Recognition (FER) models based on machine learning can only be trained with a relatively limited number of samples and whether the trained FER model can have satisfactory recognition performance is a challenge. In this paper, the facial expression database from the Laboratory Environment (LE) is used as the source domain, and the facial expression database from the wild is used as the target domain. Based on these two different databases, a hybrid improved unsupervised Cross-Domain Adaptation (...
