November 30, 2025Scientific ReportsOpen Access

Mental health assessment model for college students based on facial expression recognition

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The model achieves an accuracy of 0.945, indicating effective assessment of mental health states.
A deep learning architecture combines dynamic and static expressions for enhanced performance in recognition tasks.
Using optical flow graph and convolutional models, the method ensures fast processing with a response time of just 2.2 seconds.
This approach provides technical support for psychological interventions, enhancing student mental health response systems.

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The model demonstrates high accuracy in emotion recognition for college students, suggesting advancements in psychological interventions.

Chen et al. (2025) studied this question.

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