基于LBPH的智能考勤方法研究  

Study on Intelligent Attendance Method based on LBPH

作　　者：张文志[1] 杨森 柳广春 杜梦豪 ZHANG Wen-zhi;YANG Sen;LIU Guang-chun;DU Meng-hao(School of Surveying and Mapping and Land Information Engineering,Hennan Polytechnic University,Jiaozuo Henan 454150,China;School of Resource and Civil Engineering,Liaoning Institute of Science and Technology,Benxi Liaoning 117004,China)

机构地区：[1]河南理工大学测绘与国土信息工程学院,河南焦作454150 [2]辽宁科技学院资源与土木工程学院,辽宁本溪117004

出　　处：《辽宁科技学院学报》2022年第3期30-33,共4页Journal of Liaoning Institute of Science and Technology

基　　金：河南省科技攻关项目“高速铁路循环动荷载作用下采空区场地活化变形灾变风险评价”(212102310404).

摘　　要：针对高校学生较多,传统人工考勤方式效率低,易出现学生代签、早退等问题,鉴于此提出一种基于Local Binary Pattern Histogram(LBPH)的课堂考勤方法。该方法采用LBPH人脸识别算法,对考勤学生进行人脸识别,成功识别出勤学生,并对照人脸数据库,未识别的学生则为缺勤学生。再根据Hough变换检测和Canny边缘检测,绘制教室座位格网,并结合考勤学生人脸识别结果,实现座位与学生相匹配。在两者基础上,基于Python语言开发了课堂考勤系统。实验结果表明,该方法具有较高的人脸识别率,能有效完成课堂考勤工作,提高考勤效率。为更好推进考勤管理高效化、智能化建设提供新的思路。In view of the large number of college students,the low efficiency of the traditional manual attendance method,and the problems of student signing on behalf and leaving early,a classroom attendance method based on local binary pattern histogram(LBPH)is proposed.In this method,LBPH face recognition algorithm is used to recognize the faces of attendance students.The attendance students are successfully identified.Compared with the face database,the unrecognized students are absent students.Then,according to Hough transform detection and Canny edge detection,the classroom seat grid is drawn,and combined with the face recognition results of attendance students,the seat is then matched with students.On the basis of the two approaches,a classroom attendance system is developed based on Python language.Experimental results show that this method has a high face recognition rate,and can effectively complete the work of classroom attendance and improve the efficiency of attendance.It provides new ideas for better promoting the efficient and intelligent construction of attendance management.

关 键 词：Python-OpenCV LBPH人脸识别 HOUGH变换 CANNY边缘检测 图像处理 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]