  In the facial expression recognition task, researchers always get low
accuracy of expression classification due to a small amount of training
samples. In order to solve this kind of problem, we proposes a new data
augmentation method named MixCut. In this method, we firstly interpolate the
two original training samples at the pixel level in a random ratio to generate
new samples. Then, pixel removal is performed in random square regions on the
new samples to generate the final training samples. We evaluated the MixCut
method on Fer2013Plus and RAF-DB. With MixCut, we achieved 85.63% accuracy in
eight-label classification on Fer2013Plus and 87.88% accuracy in seven-label
classification on RAF-DB, effectively improving the classification accuracy of
facial expression image recognition. Meanwhile, on Fer2013Plus, MixCut achieved
performance improvements of +0.59%, +0.36%, and +0.39% compared to the other
three data augmentation methods: CutOut, Mixup, and CutMix, respectively.
MixCut improves classification accuracy on RAF-DB by +0.22%, +0.65%, and +0.5%
over these three data augmentation methods.
