  Facial expression is one of the most external indications of a person's
feelings and emotions. In daily conversation, according to the psychologist,
only 7% and 38% of information is communicated through words and sounds
respective, while up to 55% is through facial expression. It plays an important
role in coordinating interpersonal relationships. Ekman and Friesen recognized
six essential emotions in the nineteenth century depending on a cross-cultural
study, which indicated that people feel each basic emotion in the same fashion
despite culture. As a branch of the field of analyzing sentiment, facial
expression recognition offers broad application prospects in a variety of
domains, including the interaction between humans and computers, healthcare,
and behavior monitoring. Therefore, many researchers have devoted themselves to
facial expression recognition. In this paper, an effective hybrid data
augmentation method is used. This approach is operated on two public datasets,
and four benchmark models see some remarkable results.
