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Random Forest Regression for continuous affect using Facial Action Units

arXiv 2203.12818 ↗First posted 24 Mar 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

In this paper we describe our approach to the arousal and valence track of the 3rd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). We extracted facial features using OpenFace and used them to train a multiple output random forest regressor. Our approach performed comparable to the baseline approach.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 24 Mar 2022.

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8 of 15 references.

References · 8

  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
  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. 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
  7. 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
  8. 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155

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