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Open Access

Smart Attendance System Based on Frequency Distribution Algorithm with Passive RFID Tags

Qianwen MiaoFu Xiao( )Haiping HuangLijuan SunRuchuan Wang
School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210046, China.
Department of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210046, China.
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Abstract

Staff attendance information has always been an important part of corporate management. However, some opportunistic employees may consign others to punch their time cards, which hampers the authenticity of attendance and effectiveness of record keeping. Hence, it is necessary to develop an innovative anti-cheating system for office attendance. Radio-Frequency IDentification (RFID) offers new solutions to solve such problems because of its strong anti-interference capability and non-intrusiveness. In this paper, we present a smart attendance system that extracts distinguishable phase characteristics of individuals to enable recognition of various targets. A frequency distribution histogram is extracted as a fingerprint for recognition and the K-means clustering method is utilized for more fine-grained recognition of targets with similar features. Compared with traditional attendance mechanisms, RFID-based attendance systems are based on living biological characteristics, which greatly reduces the possibility of false records. To evaluate the performance of our system, we conducted extensive experiments. The results of which demonstrate the efficiency and accuracy of our system with an average accuracy of 92%. Moreover, the system evaluation shows that our design is robust against differences in the clothing worn and time of day, which further verifies the successful performance of our system.

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Tsinghua Science and Technology
Pages 217-226
Cite this article:
Miao Q, Xiao F, Huang H, et al. Smart Attendance System Based on Frequency Distribution Algorithm with Passive RFID Tags. Tsinghua Science and Technology, 2020, 25(2): 217-226. https://doi.org/10.26599/TST.2018.9010141

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Received: 28 September 2018
Revised: 26 November 2018
Accepted: 27 November 2018
Published: 02 September 2019
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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