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

2020/02/21 by Nhu-Tai Do, Do, Nhu-Tai, Tram-Tran Nguyen-Quynh +3
Psychology · Computer Science · #Emotion and Mood Recognition #Face recognition and analysis #Human Pose and Action Recognition

paper · pdf · doi:10.48550/arxiv.2002.09120

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 0.543 and valence-arousal score 0.534 on the validation set.

Citations

• Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition

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