Semiautomated Class Attendance Monitoring Using Smartphone Technology

Louise Cronjé, I. Sanders
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引用次数: 4

Abstract

Class attendance is important. Class attendance recording is often done using ‘roll-call’ or signing attendance registers. These are time consuming, easy to cheat and it is difficult to draw any information from them. There are other, expensive alternatives to automate attendance recording with varying accuracy. This study experimented with a smart phone camera and different combinations of face detection and recognition algorithms to determine if it can be used to record attendance successfully, while keeping the solution cost-effective. The effect of different class sizes was also investigated. The research was done within a pragmatism philosophy, using a prototype in a field experiment. The algorithms that were used, are: Viola-Jones (HAAR features), Deep Neural Network (DNN) and Histogram of Oriented Gradients (HOG) for detection and Eigenfaces, Fisherfaces and Local Binary Pattern Histogram (LBPH) for recognition. The best combination was Viola-Jones combined with Fisherfaces, with a mean accuracy of 54% for a class of 10 students and 34.5% for a class of 22 students. The best all over performance on a single class photo was 70% (class size 10). As is, this prototype is not accurate enough to use, but with a few adjustments, it may become a cheap, easy-to-implement solution to the attendance recording problem.
使用智能手机技术的半自动考勤监控
上课很重要。课堂出勤记录通常使用“点名”或在出勤登记簿上签名来完成。这些都很耗时,很容易作弊,而且很难从中获取任何信息。还有其他昂贵的替代方案可以以不同的精度自动记录考勤。这项研究用智能手机摄像头和不同的人脸检测和识别算法组合进行了实验,以确定它是否可以用于成功记录出勤情况,同时保持解决方案的成本效益。还研究了不同班级规模的影响。这项研究是在实用主义哲学的范围内进行的,使用了一个实地实验的原型。使用的算法有:用于检测的Viola Jones(HAAR特征)、深度神经网络(DNN)和面向梯度直方图(HOG),以及用于识别的特征面、Fisherfaces和局部二进制模式直方图(LBPH)。最佳组合是Viola Jones与Fisherfaces的组合,10名学生的平均准确率为54%,22名学生的准确率为34.5%。一张班级照片的整体表现最好的是70%(班级尺寸10)。事实上,这个原型使用起来不够准确,但经过一些调整,它可能会成为一个廉价、易于实现的考勤问题解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.70
自引率
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