EVALUATION OF MICRO-EXPRESSION RECOGNITION TECHNOLOGY FOR DEPRESSION EARLY WARNING SYSTEM IN PSYCHOLOGICAL INTERVENTIONS

Depression is a major global public health concern, affecting approximately 280 million people worldwide, according to the World Health Organization. This condition profoundly disrupts individuals' work, education, and family life, with severe cases leading to suicide. Self-report questionnaires, clinical interviews, and physiological assessments are the main ways that depression is currently screened for and diagnosed. However, these methods have significant flaws, such as being subjective and relying on people to report their feelings honestly. To enhance the accuracy and efficiency of early depression detection, this study recruited 1,073 first-year high school students from Wannian No. 1 High School in Wannian County, Shangrao City, Jiangxi Province. By integrating micro-expression recognition technology with the CES-D questionnaire, we developed an early warning system for depression based on micro-expression recognition, aiming to provide a more objective and automated screening approach.