| Title |
Emotional Interaction in Facial Expression Recognition for Emotional Deficiency in Remote Virtual Teaching |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.1.68 |
| Keywords |
Virtual teaching; Emotional interaction; Facial expression recognition; Self-cure nerves; Incentive factors |
| Abstract |
Traditional remote virtual teaching lacks emotional interaction like face-to-face teaching, affecting learning and teaching effectiveness in distance education. In view of this, the study innovatively introduces a self-cure neural network on the basis of convolutional network. Then, calibration strategies, regularization sorting, and noise labeling operations are used to optimize the network threshold, proposing a new facial expression recognition model. In addition, emotional space and emotional transfer pathways are constructed, and hidden Markov models, forward backward algorithms, and motivational factors are introduced to propose a motivational interaction model for emotional regression. The experimental results showed that the highest recognition accuracy of the expression recognition model was 95.8%. The recognition error was the lowest at 33% when the label noise ratio was 70%. The lowest misidentification rate was 34% when the obstruction proportion was 67%. The average emotional intensity of the incentive interaction model in multiple environments was 0.074. The average compensation time for multiple incentive factors was 13 minutes, which was 6 minutes shorter than that of a single incentive factor. The above results indicate that the proposed model can achieve accurate facial expression recognition, providing technical support for emotional interaction between teachers and students. |