人脸检测和表情识别在课堂教学评价中的应用研究

The Application Research of Face Detection and Facial Expression Recognition in Classroom Teaching Evaluation

传统课堂教学评价往往效率低下,并带有较强的主观性。本文针对传统课堂评价中存在的不足,结合深度学习技术,在CNN模型基础上建立起适合课堂场景的人脸检测和表情识别模型,得到比较准确的人脸特征,接着使用朴素贝叶斯分类器对得到的人脸特征进行分类和评价,然后研究面部特征与课堂质量之间的关系,最后建立起基于人脸检测和表情识别的课堂评价规则。实验数据表明,本研究可以作为课堂教学评价的重要参考指标。

Traditional classroom teaching evaluation is often inefficient and subjective.Aiming at the shortcomings of traditional classroom evaluation and combining with deep learning technology,this paper builds a face detection and expression recognition model suitable for classroom scene based on CNN model,and obtains more accurate facial features.Then it uses the Naive Bayesian Classifier for face feature classification and evaluation and study the relationship between facial features and the quality of classroom.Finally,this paper establishes classroom evaluation rules based on face detection and expression recognition.Experimental data shows that this study can be used as an important reference index for classroom teaching evaluation.