Mid-level Representation Enhancement and Graph Embedded Uncertainty Suppressing for Facial Expression Recognition
This work addresses facial expression recognition for applications in human-computer interaction, but it appears incremental as it builds on existing methods to handle variations and uncertainties.
The paper tackled challenges in facial expression recognition due to variations in expression patterns and data uncertainties by proposing mid-level representation enhancement and graph embedded uncertainty suppressing, achieving verified effectiveness on the Aff-Wild2 dataset.
Facial expression is an essential factor in conveying human emotional states and intentions. Although remarkable advancement has been made in facial expression recognition (FER) task, challenges due to large variations of expression patterns and unavoidable data uncertainties still remain. In this paper, we propose mid-level representation enhancement (MRE) and graph embedded uncertainty suppressing (GUS) addressing these issues. On one hand, MRE is introduced to avoid expression representation learning being dominated by a limited number of highly discriminative patterns. On the other hand, GUS is introduced to suppress the feature ambiguity in the representation space. The proposed method not only has stronger generalization capability to handle different variations of expression patterns but also more robustness to capture expression representations. Experimental evaluation on Aff-Wild2 have verified the effectiveness of the proposed method.