基于人脸检测的大学课堂关注度研究

The Research of College Classroom Focus Based on face Detection

【目的】利用深度学习的人脸检测技术为课堂教学评价提供新的方案。【方法】构建适用于课堂教学的人脸检测级联卷积神经网络模型,并进行相应优化,提出了基于统计人脸检测的抬头率来量化课堂关注度的方法。【结果】通过检测和统计课堂视频中的人脸,计算出学生的抬头率,统计出学生的课堂专注度以及时间分布,帮助教师及时准确地了解课堂教学情况。【结论】通过大量的测试和优化,该系统在人脸检测中具有较好的有效性和可靠性,可以为学生提供个性化教学,同时为教师提升课堂教学质量和教学效率提供参考。

[Purposes]Based on the technology of deep learning face detection,a new project is provided for classroom teaching evaluation.[Methods]Combined with face detection,it constructs classroom appropriate face detection optimized cascade convolutional neural mode,and proposes a new method to analyze class focus based on head-lift rate.[Findings]Through the detection and numeration of the students’faces in the video,the head-lift rate is calculated to analyze the class focus and time allocation,in order to help the teachers to be acquainted with the class teaching situation.[Conclusions]With a multitude of tests and optimization,this system possesses validity and reliability in face detection,which also offers an individualized learning for students and enhances the classroom teaching quality and efficiency for teachers.