Random Forest Regression for continuous affect using Facial Action Units
arXiv:2203.12818v3h-index: 14
Originality Synthesis-oriented
AI Analysis
This work addresses emotion recognition in-the-wild for applications like human-computer interaction, but it is incremental.
The authors tackled continuous affect prediction for arousal and valence using facial action units, achieving performance comparable to the baseline in the ABAW competition.
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.