Application of Facial Expression Using YOLOv11 to Measure Interest in Training Materials
About this article
Keywords:
Facial Expression Recognition, YOLOv11, Interest Detection, Training Materials Evaluation, Deep Learning in EducationAbstract
The purpose of this study is to assess trainees' proficiency with the Convolutional Neural Network (CNN) model in recognizing emotions from facial expressions. A wide range of applications in training, education, and human-machine interaction could benefit greatly from computer vision-based emotion recognition. A CNN model created especially to identify important emotions including happiness, sadness, anger, and fear is trained and tested in this study using a dataset of facial expressions. The test findings demonstrate that the CNN model can accurately and efficiently classify emotions and offer valuable information about trainees' strengths and limitations in identifying different emotions. This study emphasizes how CNN-based technology may be used to help assess and enhance emotion recognition skills in the setting of professional training.
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