A Proposal of Anxiety Measurement Method Using Facial Expression Transition Images Based on Deep Learning

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.