In remote intelligent teaching, the facial expression features can be recorded in time through facial recognition, which is convenient for teachers to judge the learning status of students in time and helps teachers to change teaching strategies in a timely manner. Based on this, this study applies machine learning and virtual reality technology to distance classroom teaching. Moreover, this study uses different channels to automatically learn global and local features related to facial expression recognition tasks. In addition, this study integrates the soft attention mechanism into the proposed model so that the model automatically learns the feature maps that are more important for facial expression recognition and the salient regions within the feature maps. At the same time, this study performs weighted fusion on the features extracted from different branches, and uses the fused features to re-recognize student features. Finally, this study analyzes the results of this paper through control experiments. The research results show that the algorithm proposed in this paper has good performance and can be applied to the distance teaching system.
A Machine Learning Algorithm of Human-Computer-Interface Application-An AdaRank Model Approach to Facial Expression Recognition
