Classroom student emotion recognition using an improved segmentation clustering and multi-feature fusion emotion recognition algorithm

Key Points

• This research aims to improve emotion recognition among students in classroom settings using advanced algorithms.
• Utilized an improved segmentation clustering approach for data analysis.
• Applied multi-feature fusion techniques to enhance emotion recognition accuracy.
• Implemented the algorithm in diverse classroom environments to test effectiveness.
• Achieved an accuracy rate of 85% in emotion classification across various classroom scenarios.
• Improved student engagement metrics were observed following the implementation of the emotion recognition algorithm.

Abstract