Face　expression　plays　a　critical　role　during　the　daily　life,　and　people　cannot　live　without　face　emotion.　With　the　development　of　technology,　many　methods　of　facial　expression　recognition　have　been　proposed.　However,　from　traditional　methods　to　deep　learning　methods,　few　of　them　pay　attention　to　the　hybrid　data　augmentation,　which　can　help　improve　the　robustness　of　models.　Therefore,　a　method　of　hybrid　data　augmentation　is　highlighted　in　this　paper.　The　hybrid　data　augmentation　is　a　method　of　combining　several　effective　data　augmentation.　In　the　experiments,　the　technique　is　applied　on　four　basic　networks　and　the　results　are　compared　to　the　baseline　models.　After　applying　this　technique,　the　results　show　that　four　benchmark　models　have　higher　performance　than　those　previously.　This　approach　is　simple　and　robust　in　terms　of　data　augmentation,　which　makes　it　applicated　in　the　real　world　in　the　future.　Besides　the　results　show　versatility　of　the　technique　as　all　of　our　experiments　get　better　results.　©　2022　ACM.