A Proposal of Anxiety Measurement Method Using Facial Expression Transition Images Based on Deep Learning
Year: 2024
Type: article
Abstract: With anxiety disorders on the rise in recent years, early detection and assessment of anxiety states is extremely important. Conventional approaches have mainly used self-reported questionnaires, but they have reliability problems. Therefore, this study aims to generate facial images that include minute changes in facial expression using deep learning techniques to more accurately and simply assess subjects’ anxiety states. This method quantitatively evaluates the degree of anxiety state by analyzing the subject’s facial expression recognition of the generated images. The validity of this method was verified through correlation analysis with the results of the State-Trait Anxiety Inventory (STAI), an existing anxiety assessment tool. The results showed a significant correlation between the facial expression recognition score and the STAI score, indicating that this method is effective in assessing anxiety states. This study aims to innovate the evaluation method of anxiety state by integrating deep learning and psychology.
Authors: Sota WATANABE, Makoto HASEGAWA
Institution: Tokyo Denki University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4402163835