Design and Implementation of IoT Based Class Attendance Monitoring System Using Computer Vision and Embedded Linux Platform
To provide reliable, time-saving and automatic class attendance system, the concept of Internet of Things (IoT) based class attendance monitoring system using embedded Linux platform is presented in this paper. The study is focused on the design and implementation of face detection and recognition system using Raspberry Pi. The system takes images of students, and analyzes, detects and recognizes faces using image processing algorithms, where the Haar cascade classifier algorithm is implemented to detect faces and local binary pattern histogram algorithm is used to recognize these faces. After collecting image processing data, the system generates a final attendance record and uploads it in a cloud server. The cloud server has been implemented using python based web framework. The record can be accessed remotely from a user-friendly, web application using the Internet. Finally, the system is also capable of sending an email notification with the final record to the teachers and students in a specific time. Tests and performance analysis were done to verify the efficiency of this system.
KeywordsIoT Embedded Linux platform Haar Cascade Classifier Raspberry Pi Class attendance
This work is financially supported by the National Natural Science Foundation of P. R. China (No.: 61672296, No.: 61602261), Scientific & Technological Support Project of Jiangsu Province (No.: BE2015702, BE2016185, No.: BE2016777), Postgraduate Research and Practice Innovation Program of Jiangsu Province (No.: KYCX17_0798).
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