  Representation learning and feature disentanglement have recently attracted
much research interests in facial expression recognition. The ubiquitous
ambiguity of emotion labels is detrimental to those methods based on
conventional supervised representation learning. Meanwhile, directly learning
the mapping from a facial expression image to an emotion label lacks explicit
supervision signals of facial details. In this paper, we propose a novel FER
model, called Poker Face Vision Transformer or PF-ViT, to separate and
recognize the disturbance-agnostic emotion from a static facial image via
generating its corresponding poker face without the need for paired images.
Here, we regard an expressive face as the comprehensive result of a set of
facial muscle movements on one's poker face (i.e., emotionless face), inspired
by Facial Action Coding System. The proposed PF-ViT leverages vanilla Vision
Transformers, and are firstly pre-trained as Masked Autoencoders on a large
facial expression dataset without emotion labels, obtaining excellent
representations. It mainly consists of five components: 1) an encoder mapping
the facial expression to a complete representation, 2) a separator decomposing
the representation into an emotional component and an orthogonal residue, 3) a
generator that can reconstruct the expressive face and synthesize the poker
face, 4) a discriminator distinguishing the fake face produced by the
generator, trained adversarially with the encoder and generator, 5) a
classification head recognizing the emotion. Quantitative and qualitative
results demonstrate the effectiveness of our method, which trumps the
state-of-the-art methods on four popular FER testing sets.
