摘 要:借助于计算机视觉与模式识别技术,主要探讨智慧课堂中人脸识别考勤和课堂行为分析等技术,通过 Jakarta EE平台,设计并开发智慧课堂分析系统,实现课堂考勤、课堂督查以及课堂分析。提出了一种适用于学习分析的专注度评估方法。该方法给出了出勤率、抬头率、疲劳程度的计算方法,并建立了综合评估学生的课堂专注度的模型。系统可以实现对学生上课数据的采集与自动分析,并生成面向校方与老师的可视化数据报表。
关键词:人脸识别;智慧课堂;Jakarta EE
DOI:10.19850/j.cnki.2096-4706.2022.04.006
基金项目:南京工业大学浦江学院校级科研项目(Njpj2020-1-03)
中图分类号:TP311 文献标识码:A 文章编号:2096-4706(2022)04-0024-04
Design and Implementation of Smart Classroom Analysis System Based on Face Recognition
JIANG Lili, Wang Shuting
(Nanjing Tech University Pujiang Institute, Nanjing 211200, China)
Abstract: With the help of computer vision and pattern recognition technology, this paper mainly discusses the technologies of face recognition attendance and classroom behavior analysis and so on in smart classroom. Through Jakarta EE platform, this paper designs and develops smart classroom analysis system to realize classroom attendance, classroom supervision and classroom analysis. A focus evaluation method suitable for learning analysis is proposed. This method gives the calculation methods of attendance rate, head up rate and fatigue degree, and establishes a model for comprehensively evaluating students’ classroom concentration degree. The system can realize the collection and automatic analysis of students’ class data, and generate visual data reports for the school and teachers.
Keywords: face recognition; smart classroom; Jakarta EE
参考文献:
[1] 刘邦奇 .“互联网 +”时代智慧课堂教学设计与实施策略研究 [J]. 中国电化教育,2016(10):51-56+73.
[2] 邵燕楠,黄燕宁 . 学情分析:教学研究的重要生长点 [J].中国教育学刊,2013(2):60-63.
[3] DENG J K,GUO J,ZHOU Y X,et al.RetinaFace:Singlestage Dense Face Localisation in the Wild [J/OL].arXiv:1905.00641 [cs.CV].[2021-12-03].https://arxiv.org/abs/1905.00641.
[4] DENG J K,GUO J,XUE N N,et al.ArcFace:Additive Angular Margin Loss for Deep Face Recognition [J/OL].arXiv:1801.07698 [cs.CV].[2021-12-06].https://arxiv.org/abs/1801.07698.
[5] LU Z,PU H M,WANG F C,et al.The Expressive Power of Neural Networks:A View from the Width [J/OL].arXiv:1709.02540 [cs. LG].[2021-12-08].https://arxiv.org/abs/1905.00641.
[6] HE K,ZHANG X,REN S,et al.Deep Residual Learning for Image Recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Las Vegas:CVPR,2016:770-778.
[7] 辛园园,钮俊,谢志军,等 . 微服务体系结构实现框架综述 [J]. 计算机工程与应用,2018,54(19):10-17.
[8] 易文康,程骅,程耕国 .Shiro 框架在 Web 系统安全性上的改进与应用 [J]. 计算机工程,2018,44(11):135-139.
[9] DINGES D F,GRACE R.PERCLOS:A V a l i d Psychophysiological Measure of Alertness as Assessed by Psychomotor Vigilance [J].Tech Brief,1998,31(5):1237-1252.
作者简介:姜丽莉(1985—),女,汉族,江苏徐州人,讲师,硕士,研究方向:深度学习、数据挖掘、软件工程;王澍廷(1998—),男,汉族,广东深圳人,本科,研究方向:深度学习、软件工程。