mlsanityData to 13 Oct 2026

SS-MFAR : Semi-supervised Multi-task Facial Affect Recognition

arXiv 2207.09012 ↗First posted 19 Jul 2022cs.CVcited by 5

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Abstract (arXiv)

Automatic affect recognition has applications in many areas such as education, gaming, software development, automotives, medical care, etc. but it is non trivial task to achieve appreciable performance on in-the-wild data sets. In-the-wild data sets though represent real-world scenarios better than synthetic data sets, the former ones suffer from the problem of incomplete labels. Inspired by semi-supervised learning, in this paper, we introduce our submission to the Multi-Task-Learning Challenge at the 4th Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition. The three tasks that are considered in this challenge are valence-arousal(VA) estimation, classification of expressions into 6 basic (anger, disgust, fear, happiness, sadness, surprise), neutral, and the 'other' category and 12 action units(AU) numbered AU-{1,2,4,6,7,10,12,15,23,24,25,26}. Our method Semi-supervised Multi-task Facial Affect Recognition titled SS-MFAR uses a deep residual network with task specific classifiers for each of the tasks along with adaptive thresholds for each expression class and semi-supervised learning for the incomplete labels. Source code is available at https://github.com/1980x/ABAW2022DMACS.

Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 19 Jul 2022.

Builds on

16 of 34 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,950
  2. 2016MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognitioncited by 624
  3. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 491
  4. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  5. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  6. 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
  7. 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
  8. 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
  9. 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
  10. 2018Aff-Wild2: Extending the Aff-Wild Database for Affect Recognitioncited by 81
  11. 2018Facial Action Unit Detection Using Attention and Relation Learningcited by 46
  12. 2022Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margincited by 32
4 more
  1. 2018A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressionscited by 25
  2. 2021Causal affect prediction model using a facial image sequencecited by 8
  3. 2018Two-level Attention with Two-stage Multi-task Learning for Facial Emotion Recognitioncited by 7
  4. 2018A Deep Network for Arousal-Valence Emotion Prediction with Acoustic-Visual Cuescited by 2

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Cited by year (2026 to 13 Oct)

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

5 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. 2026A Shared Latent for Partially-Labeled Multi-Task Facial Affect Recognitioncited by 1
  5. 2026Strength-Parity Ensembling with Parameter-Isolated Experts for Multi-Task Affect Recognitioncited by 0