摘要
人脸识别技术是基于生物特征的身份认证的重要手段,本文探讨了利用OpenCV和卷积神经网络(Convolutional Neural Networks,CNN)实现基于人脸识别的考勤管理系统的方法。首先,对OpenCV算法和CNN技术作了分析;其次,基于考勤系统的需求,对系统功能模块设计、人脸图像采集、图像特征提取和系统功能测试等方面作了详细的阐述;最后,对系统功能进行测试,该系统能够快速准确的进行人脸识别与比对,实现数据统计分析,有效提高课堂管理与教学管理效率,同时系统可应用于不同领域的人员身份认证,具有较好的移植性与应用性。
Face recognition technology is an important means of biometric-based authentication.This paper discusses the implementation of a face recognition-based attendance management system using OpenCV and Convolutional Neural Networks(CNN).Firstly,the OpenCV algorithm and CNN technology are analyzed;secondly,based on the requirements of the attendance system,the functional module design,face image acquisition,image feature extraction and system function testing are elaborated;finally,the system functions are tested and the test results show that the system can quickly and accurately perform face recognition and comparison,realize data statistical analysis,and effectively improve The test results show that the system can quickly and accurately perform face recognition and comparison,realize data statistical analysis,and effectively improve the efficiency of classroom management and teaching management,while the system can be applied to personnel identity authentication in different fields,and has good portability and applicability.
作者
叶俊斌
汤海林
陈佳霖
阙钧平
翟敏焕
YE Junbin;TANG Hailin;CHEN Jialin;QUE Junping;ZHAI Minghuan(Faculty of Megadata and Computing,Guangdong Baiyun University,Guangzhou Guangdong 510450,China)
出处
《信息与电脑》
2022年第10期124-127,共4页
Information & Computer
基金
广东省科技创新战略项目(项目编号:pdjh2020666)
广东省普通高校青年创新人才类项目(项目编号:2018KQNCX299)。
