基于卷积神经网络的课堂状态分析系统
随着近年来人们在人脸识别领域的进一步研究发现，人脸识别已经越来越多地投入到人们的日常生活之中了.与此同时，有很多高校的学生在大学自由的氛围之中逐渐地大学知识的学习越来越不认真.因此本课题设计出一个基于人脸识别的课堂状态监测系统，旨在通过人脸识别监测学生上课时候的状态.
本系统运用python语言开发的课程学生学习行为可视分析系统，在程序中使用了CNN(卷积神经网络)进行深度学习.本程序通过对视频的读取，采用先进的图像处理和模式识别，在视频中找到表情和姿势，并且对其进行标注，进而实现对学生听课状态的监测，利用人脸移动区间约束判断学生是否离开座位、脸部朝向等信息，实现对学生听课状态的监测.在设计上采用了一些较新、较完善的设计，分析了系统的一些基本功能和组成情况,包括系统的需求分析、系统结构,功能模块划分以及模式分析等,重点对应用程序的实际开发实现作了介绍，保证了数据信息的一致性和安全性，确保应用程序功能齐全完备，符合系统的要求.
关键词：课程学生学习行为可视分析；图像识别；卷积神经网络；python；深度学习；
With the further research in the field of face recognition in recent years, it is found that face recognition has been more and more put into people’s daily life At the same time, there are many college students in the atmosphere of College freedom, gradually learning college knowledge more and more seriously Therefore, this subject designs a classroom state monitoring system based on face recognition, which aims to monitor the state of students in class through face recognition
The system uses the classroom state analysis system developed by Python language, and uses CNN (convolutional neural network) for in-depth learning in the program This program reads the video, uses advanced image processing and pattern recognition, finds the expression and posture in the video, and labels them, so as to realize the monitoring of students’ listening state. It uses the constraints of face movement interval to judge whether students leave their seats, face orientation and other information, so as to realize the monitoring of students’ listening state Some new and perfect designs are adopted in the design, and some basic functions and components of the system are analyzed, including system demand analysis, system structure, function module division and mode analysis. The actual development and implementation of the application program are introduced, which ensures the consistency and security of data information, and ensures that the application program has complete functions and meets the requirements of the system
Key words: classroom state analysis; Image recognition; Convolutional neural network; python； Deep learning;
如何让学生保持高效率的学习状态一直是各个领域的研究热点.在科技飞速进步和教育不断向前发展的趋势下，课堂教学中不断加入了多媒体、教育应用程序等科技元素.课堂教学的改革虽然已取得了巨大的进步，积累了大量成功的经验，但是仍存在诸多问题和不足.首先，在传统的教育模式下，
