基于人脸表情识别的课堂质量分析

Classroom Quality Analysis Based on Facial Expression Recognition

课堂是教师授课与学生学习的主阵地,所以课堂质量分析体现着一所学校的教学水准和教师授课于学生的的适用性的高低。然而,在当今教育业中,对于课堂质量的分析,每个学校或者每个教育机构都有着参差不齐的理论基础和评价方法,始终都没有达成一个统一且高效的标准。因此,对于结合人脸表情识别技术,研究出新的低成本或成本可控、高精度以及较高可靠性的课堂质量分析是我们现在亟需解决的难点问题。论文提出的一种基于视频序列表情识别的新模型,即特征融合-BiLSTM模型在常用的数据集中验证了表情识别的效果,并将其使用于论文给出的新的课堂质量分析体系中,结果显示可以为现阶段的课堂教学分析提供相对可靠的参照。

Classroom is the main position for teachers to teach and students to learn,so the analysis of classroom quality re⁃flects the teaching level of a school and the applicability of teachers to students.However,in today's education industry,for the anal⁃ysis of classroom quality,every school or every educational institution has uneven theoretical foundations and evaluation methods,and has never reached a unified and efficient standard.Therefore,combining facial expression recognition technology to develop a new low-cost or cost-controllable,high-precision and high-reliability classroom quality analysis is a difficult problem that we ur⁃gently need to solve.This paper proposes a new model based on video sequence expression recognition,that is,the feature fu⁃sion-BiLSTM model,which verifies the effect of expression recognition in commonly used data sets,and uses it in the new class⁃room quality analysis system given in this article.The display can provide a relatively reliable reference for the current classroom teaching analysis.