mlsanityData to 7 Oct 2026

Mid-level Representation Enhancement and Graph Embedded Uncertainty Suppressing for Facial Expression Recognition

arXiv 2207.13235 ↗First posted 27 Jul 2022cs.CV cs.AIcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 27 Jul 2022.

Builds on

15 of 27 references.

References · 15

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  3. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  4. 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
  5. 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
  6. 2020Suppressing Uncertainties for Large-Scale Facial Expression Recognitioncited by 157
  7. 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
  8. 2020AM-GCN: Adaptive Multi-channel Graph Convolutional Networkscited by 121
  9. 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
  10. 2021Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognitioncited by 89
  11. 2021Learning with Noisy Labels via Sparse Regularizationcited by 38
  12. 2018Photorealistic Facial Synthesis in the Dimensional Affect Spacecited by 33
3 more
  1. 2016Peak-Piloted Deep Network for Facial Expression Recognitioncited by 22
  2. 2019Identity-Free Facial Expression Recognition using conditional Generative Adversarial Networkcited by 14
  3. 2017Deep generative-contrastive networks for facial expression recognitioncited by 7

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Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 1.

6 papers.

  1. 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
  2. 2024The 6th Affective Behavior Analysis in-the-wild (ABAW) Competitioncited by 78
  3. 20247th ABAW Competition: Multi-Task Learning and Compound Expression Recognitioncited by 53
  4. 2023ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learningcited by 9
  5. 2025Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression Recognitioncited by 1
  6. 2024GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distributioncited by 0