The Facial Expression Data Enhancement Method Induced by Improved StarGAN V2

Abstract

Due to the small data and unbalanced sample distribution in the existing facial emotion datasets, the effect of facial expression recognition is not ideal. Traditional data augmentation methods include image angle modification, image shearing, and image scrambling. The above approaches cannot solve the problem that is the high similarity of the generated images. StarGAN V2 can generate different styles of images across multiple domains. Nevertheless, there are some defects in gener-ating these facial expression images, such as crooked mouths and fuzzy facial expression images. To service such problems, we improved StarGAN V2 by solving the drawbacks of creating pictures that apply an SENet to the generator of StarGAN V2. The generator’s SENet can concentrate at-tention on the important regions of the facial expression images. Thus, this makes the generated symmetrical expression image more obvious and easier to distinguish. Meanwhile, to further im-prove the quality of the generated pictures, we customized the hinge loss function to reconstruct the loss functions that increase the boundary of real and fake images. The created facial expression pictures testified that our improved model could solve the defects in the images created by the original StarGAN V2. The experiments were conducted on the CK+ and MMI datasets. The correct recognition rate of the facial expressions on the CK+ was 99.2031%, which is a 1.4186% higher accuracy than that of StarGAN V2. The correct recognition rate of the facial expressions on the MMI displays was 98.1378%, which is 5.059% higher than that of the StarGAN V2 method. Furthermore, contrast test outcomes proved that the improved StarGAN V2 performed better than most state-of-the-art methods.