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Representation learning and feature disentanglement have recently attractedmuch research interests in facial expression recognition. The ubiquitousambiguity of emotion labels is detrimental to those methods based onconventional supervised representation learning. Meanwhile, directly learningthe mapping from a facial expression image to an emotion label lacks explicitsupervision signals of facial details. In this paper, we propose a novel FERmodel, called Poker Face Vision Transformer or PF-ViT, to separate andrecognize the disturbance-agnostic emotion from a static facial image viagenerating its corresponding poker face without the need for paired images.Here, we regard an expressive face as the comprehensive result of a set offacial muscle movements on one's poker face (i.e., emotionless face), inspiredby Facial Action Coding System. The proposed PF-ViT leverages vanilla VisionTransformers, and are firstly pre-trained as Masked Autoencoders on a largefacial expression dataset without emotion labels, obtaining excellentrepresentations. It mainly consists of five components: 1) an encoder mappingthe facial expression to a complete representation, 2) a separator decomposingthe representation into an emotional component and an orthogonal residue, 3) agenerator that can reconstruct the expressive face and synthesize the pokerface, 4) a discriminator distinguishing the fake face produced by thegenerator, trained adversarially with the encoder and generator, 5) aclassification head recognizing the emotion. Quantitative and qualitativeresults demonstrate the effectiveness of our method, which trumps thestate-of-the-art methods on four popular FER testing sets.

Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers