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

• URL: http://arxiv.org/abs/2002.09120v3
• Date: Thu, 5 Mar 2020 08:23:46 GMT
• Title: Affective Expression Analysis in-the-wild using Multi-Task Temporal Statistical Deep Learning Model
• Authors: Nhu-Tai Do, Tram-Tran Nguyen-Quynh and Soo-Hyung Kim
• Abstract summary: We present an affective expression analysis model that deals with the above challenges. We experimented on Aff-Wild2 dataset, a large-scale dataset for ABAW Challenge.
• Score: 6.024865915538501
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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