学生课堂表情识别系统的设计与实现

Design and Implementation of a Classroom Expression Recognition System for Students

在智慧教育领域,实时监测学生课堂专注度对于提升教学质量具有十分重要的意义。本文开发一款基于表情识别的学生课堂专注度分析系统。系统基于C/S架构,采用Python编程语言和Pyside6图形界面框架,并使用Fer2013数据集对Mini-Xception网络模型进行训练和测试。测试结果表明,系统能够较为准确地辨识出学生课堂听讲期间的面部表情,从而可以辅助教师掌握学生的学习专注状态,为个性化教学的开展提供基础数据支撑。

In the field of smart education,real-time monitoring of students'classroom concentration is of great significance for improving teaching quality.This article develops a student classroom focus analysis system based on facial expression recognition.The system is based on the C/S architecture,using Python programming language and Pyside6 graphical interface framework,and training and testing the Mini Perception network model using the Fer2013 dataset.The test results show that the system can accurately identify students'facial expressions during classroom listening,which can assist teachers in grasping students'learning focus status and provide basic data support for personalized teaching.