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Affective Expression Analysis in-the-wild using Multi-Task Temporal Statistical Deep Learning Model

2020-02-21 04:06:03
Nhu-Tai Do, Soo-Hyung Kim

Abstract

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 0.533 and valence-arousal score 0.5126 on validation set.

Abstract (translated)

URL

https://arxiv.org/abs/2002.09120

PDF

https://arxiv.org/pdf/2002.09120.pdf


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