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

Understand

Representation learning and feature disentanglement have garnered significant research interest in the field of facial expression recognition (FER).

• The inherent ambiguity of emotion labels poses challenges for conventional supervised representation learning methods.
• Moreover, directly learning the mapping from a facial expression image to an emotion label lacks explicit supervision signals for capturing fine-grained facial features.
• In this paper, we propose a novel FER model, named Poker Face Vision Transformer or PF-ViT, to address these challenges.