mlsanityData to 7 Oct 2026

Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition

arXiv 2303.08617 ↗First posted 15 Mar 2023cs.CVcited by 4

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Abstract (arXiv)

Facial Expression Recognition (FER) is an important task in computer vision and has wide applications in human-computer interaction, intelligent security, emotion analysis, and other fields. However, the limited size of FER datasets limits the generalization ability of expression recognition models, resulting in ineffective model performance. To address this problem, we propose a semi-supervised learning framework that utilizes unlabeled face data to train expression recognition models effectively. Our method uses a dynamic threshold module (DTM) that can adaptively adjust the confidence threshold to fully utilize the face recognition (FR) data to generate pseudo-labels, thus improving the model's ability to model facial expressions. In the ABAW5 EXPR task, our method achieved excellent results on the official validation set.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 37 of its 67 references that are in the map. First posted on arXiv 15 Mar 2023.

Builds on

37 of 67 references.

References · 37

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25 more
  1. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  2. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  3. 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
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  5. 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
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  12. 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
  13. 2021Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognitioncited by 89
  14. 2021TransFER: Learning Relation-aware Facial Expression Representations with Transformerscited by 85
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  22. 2022Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margincited by 32
  23. 2021Learning Facial Representations from the Cycle-consistency of Facecited by 20
  24. 2021Rethinking InfoNCE: How Many Negative Samples Do You Need?cited by 18
  25. 2022Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognitioncited by 17

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Cited by year

Citing papers per year: 2024: 4.

4 papers.

  1. 2024The 6th Affective Behavior Analysis in-the-wild (ABAW) Competitioncited by 78
  2. 20247th ABAW Competition: Multi-Task Learning and Compound Expression Recognitioncited by 53
  3. 2024SynFER: Towards Boosting Facial Expression Recognition with Synthetic Datacited by 6
  4. 2024Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognitioncited by 3