Inicio  /  Applied Sciences  /  Vol: 11 Par: 11 (2021)  /  Artículo
ARTÍCULO
TITULO

Stress Analysis with Dimensions of Valence and Arousal in the Wild

Thi-Dung Tran    
Junghee Kim    
Ngoc-Huynh Ho    
Hyung-Jeong Yang    
Sudarshan Pant    
Soo-Hyung Kim and Guee-Sang Lee    

Resumen

In the field of stress recognition, the majority of research has conducted experiments on datasets collected from controlled environments with limited stressors. As these datasets cannot represent real-world scenarios, stress identification and analysis are difficult. There is a dire need for reliable, large datasets that are specifically acquired for stress emotion with varying degrees of expression for this task. In this paper, we introduced a dataset for Stress Analysis with Dimensions of Valence and Arousal of Korean Movie in Wild (SADVAW), which includes video clips with diversity in facial expressions from different Korean movies. The SADVAW dataset contains continuous dimensions of valence and arousal. We presented a detailed statistical analysis of the dataset. We also analyzed the correlation between stress and continuous dimensions. Moreover, using the SADVAW dataset, we trained a deep learning-based model for stress recognition.

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