基于人脸识别的智能课堂考勤系统设计

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI:10.1109/ITNEC.2019.8729496

Wenxian Zeng, Qinglin Meng, Ran Li

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引用次数: 23

摘要

我国高校课堂出勤方式费时费力，出勤成本过高。在本文中，我们使用深度学习的相关思想来改进AlexNet卷积神经网络，并使用WebFace数据集来改进网络的训练和测试。前5名的错误率仅为6.73%。我们将该模型应用于人脸识别，并结合RFID卡读取技术，开发了基于人脸识别的智能课堂考勤系统。研究表明，该系统高效稳定，有效降低了课堂出勤成本。

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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Design of Intelligent Classroom Attendance System Based on Face Recognition

It is time-consuming and laborious for classroom attendance methods in Chinese universities, and the attendance costs are too high. In this paper, we use the deep learning related ideas to improve the AlexNet convolutional neural network, and use the WebFace data set to improve the network training and test. The Top-5 error rate is only 6.73%. We applied this model to face recognition and combined with RFID card reading technology, which developed a smart classroom attendance system based on face recognition. Research shows that the system is efficient and stable, which effectively reduce classroom attendance costs.

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来源期刊

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

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