mlsanityData to 14 Oct 2026

Facial Expression Recognition based on Multi-head Cross Attention Network

arXiv 2203.13235 ↗First posted 24 Mar 2022cs.CVcited by 8

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Abstract (arXiv)

Facial expression in-the-wild is essential for various interactive computing domains. In this paper, we proposed an extended version of DAN model to address the VA estimation and facial expression challenges introduced in ABAW 2022. Our method produced preliminary results of 0.44 of mean CCC value for the VA estimation task, and 0.33 of the average F1 score for the expression classification task.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 11 of its 16 references that are in the map. First posted on arXiv 24 Mar 2022.

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11 of 16 references.

References · 11

  1. 2017Focal Loss for Dense Object Detectioncited by 752
  2. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 492
  3. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  4. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  5. 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
  6. 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
  7. 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
  8. 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
  9. 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
  10. 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155
  11. 2015Learning Social Relation Traits from Face Imagescited by 25

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 2; 2025: 0; 2026: 1.

8 papers.