  Facial expression recognition (FER) plays a significant role in the
ubiquitous application of computer vision. We revisit this problem with a new
perspective on whether it can acquire useful representations that improve FER
performance in the image generation process, and propose a novel generative
method based on the image inversion mechanism for the FER task, termed
Inversion FER (IFER). Particularly, we devise a novel Adversarial Style
Inversion Transformer (ASIT) towards IFER to comprehensively extract features
of generated facial images. In addition, ASIT is equipped with an image
inversion discriminator that measures the cosine similarity of semantic
features between source and generated images, constrained by a distribution
alignment loss. Finally, we introduce a feature modulation module to fuse the
structural code and latent codes from ASIT for the subsequent FER work. We
extensively evaluate ASIT on facial datasets such as FFHQ and CelebA-HQ,
showing that our approach achieves state-of-the-art facial inversion
performance. IFER also achieves competitive results in facial expression
recognition datasets such as RAF-DB, SFEW and AffectNet. The code and models
are available at https://github.com/Talented-Q/IFER-master.
