基于局部二值模式直方图的学生考勤系统人脸识别

A. D. Alexander, Ratna Salkiawati, Hendarman Lubis, Fathur Rahman, Herlawati Herlawati, R. Handayanto
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引用次数: 0

摘要

学生出勤记录在教育过程中具有重要的作用。雅加达大学(Universitas Bhayangkara Jakarta Raya)作为一个案例研究,将出勤记录作为最终成绩计算的因素。许多考勤系统采用生物识别技术开发,如面部识别、虹膜识别和指纹识别。在本研究中,由于人脸不能被复制,可以消除学生的欺诈行为,因此提出了人脸识别。此外,这种非接触式方法可以通过一些额外的治疗来最大限度地降低COVID-19传播的风险。本研究提出了局部二元模式(LBP)。该方法通过将图像划分为特征提取的小部分来描述图像的纹理和形状。结果表明,该系统识别学生的准确率为86%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Local Binary Pattern Histogram for Face Recognition in Student Attendance System
Student attendance record has an important role in the educational process. Universitas Bhayangkara Jakarta Raya, as a case study, uses attendance record as the factor for final grade calculation. Many attendance recording systems were developed using biometrics, e.g. face recognition, iris recognition, and fingerprint recognition. In this study, face recognition was proposed since the face cannot be duplicated and can eliminate fraud committed by students. In addition, this contactless method could minimize the risk of COVID-19 spread with some additional treatments. The local binary pattern (LBP) was proposed in this study. This method has the ability to describe the texture and shape of an image by dividing the image into small portions of feature extraction. The result showed that the proposed system can identify students with 86% accuracy.
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