Application of Facial Expression Using YOLOv11 to Measure Interest in Training Materials

Abstract

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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