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

作     者：Qianwen Miao Fu Xiao Haiping Huang Lijuan Sun Ruchuan Wang Qianwen Miao;Fu Xiao;Haiping Huang;Lijuan Sun;Ruchuan Wang

作者机构：School of ComputerNanjing University of Posts and TelecommunicationsNanjing 210046China Department of Jiangsu High Technology Research Key Laboratory for Wireless Sensor NetworksNanjing 210046China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2020年第25卷第2期

页      面：217-226页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported in part by the National Key Basic Research and Development (973) Program of China (No.2018YFB0803400) the Nature Science Foundation of Jiangsu for Distinguished Young Scientist (No.BK20170039) the National Natural Science Foundation of China (Nos.61702283,61873131,and 61872196) 

主　　题：frequency distribution Radio-Frequency IDentification(RFID) office attendance K-means clustering 

摘      要：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.