A study on the application of multidimensional feature fusion attention mechanism based on sight detection and emotion recognition in online teaching

Abstract

Abstract Online teaching is not limited by time, but the problem of low learning efficiency is common. To address this problem, the study proposes an attention mechanism for multidimensional feature fusion, which first detects faces, uses a supervised gradient descent algorithm for face feature point detection, and improves the least-squares ellipse-fitting algorithm to detect the open/closed state of human eyes. The sight detection method is also improved, and the fuzzy inference method is used to identify students’ emotions, and the modules are fused to achieve multidimensional feature fusion attention detection for online teaching. The study found that the average accuracy rate was 84.5% with glasses and 92.0% without glasses. The research method with glasses had an average time consumption of 17 ms, while the method without glasses took 15 ms, indicating higher detection accuracy and faster real-time performance. The improved approach led to higher recognition accuracy and accuracy rate. The detection accuracy of a single feature and the research method was 74.1 and 91.9%, respectively. It shows that the research method helps in the detection of students’ attention in online teaching.