Affective Expression Analysis in-the-wild using Multi-Task Temporal Statistical Deep Learning Model
This work addresses affective behavior analysis for applications like human-computer interaction and health monitoring, but it appears incremental as it builds on existing datasets and challenges.
The paper tackled the challenge of classifying basic emotions and regressing valence-arousal values in uncontrolled environments using the Aff-Wild2 dataset, achieving expression and valence-arousal scores of 0.543 and 0.534 on the validation set.
Affective behavior analysis plays an important role in human-computer interaction, customer marketing, health monitoring. ABAW Challenge and Aff-Wild2 dataset raise the new challenge for classifying basic emotions and regression valence-arousal value under in-the-wild environments. In this paper, we present an affective expression analysis model that deals with the above challenges. Our approach includes STAT and Temporal Module for fine-tuning again face feature model. We experimented on Aff-Wild2 dataset, a large-scale dataset for ABAW Challenge with the annotations for both the categorical and valence-arousal emotion. We achieved the expression score 0.543 and valence-arousal score 0.534 on the validation set.