Automated Assessment of Student Mental Health Through Image Processing Technology

Students in vocational education face high levels of academic and employment pressure, significantly impacting their mental health, academic performance, and career development.Traditional mental health assessment methods, relying on questionnaires or interviews, often lag in timeliness and are limited in their ability to reflect real-time changes in students' mental states.Recently, the application of image processing technology in mental health monitoring has gained attention, as it allows for faster, more accurate detection of emotional changes by capturing features like facial expressions and postural behaviors.However, existing approaches often focus on singular emotional features or are limited to static images, failing to leverage the combined potential of dynamic information and subtle facial expressions.This paper proposes a dual-analysis method based on temporal action detection and micro-expression recognition to comprehensively assess students' body language and emotional changes.This approach enables accurate monitoring of mental health status, providing technical support for a psychological support system tailored to vocational education students.