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http://dx.doi.org/10.6109/jkiice.2022.26.3.333

Exploration of deep learning facial motions recognition technology in college students' mental health  

Li, Bo (Department of Psychological Counseling, Paichai University)
Cho, Kyung-Duk (Department of Psychology & Counselling, Paichai University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.3, 2022 , pp. 333-340 More about this Journal
Abstract
The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.
Keywords
Deep learning; Emotion recognition; Students' mental health;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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