Publication Type:

Conference Paper

Source:

Proceedings - IEEE 8th International Conference on Technology for Education, T4E 2016, Institute of Electrical and Electronics Engineers Inc., p.62-67 (2017)

ISBN:

9781509061150

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013328712&doi=10.1109%2fT4E.2016.021&partnerID=40&md5=4690e5c4be126e691d8d56149b8c7569

Keywords:

Academic performance, Attendance, Attendance monitoring, Automation, Classroom environment, Education, Face recognition, Fingerprinting, Hardware, Indoor positioning, Positioning accuracy, Signal encoding, Signal interference, Smartphones, Specialized hardware, Students, Wi-Fi, Wireless local area networks (WLAN)

Abstract:

Academic performance is directly affected by student attendance during the lecture hours. There are existing manual and automated attendance tracking systems that work to ensure that students attend the lectures without fail. However, the practical implementation of most automated systems have drawbacks such as high monetary cost, the need to install specialized hardware, and proneness to fake or proxy attendance. To address this, we propose a novel attendance marking system with which students may mark attendance using their smartphones. While applying facial recognition via the smartphone's front camera to determine the student's identity, the system also makes use of the campus Wi-Fi network to determine the student's location. The proposed system does not require high monetary cost or specialized hardware and yet incorporates adequate foolproof measures to counter fake or proxy attendance. Experimental studies with our system show that fingerprinting, which is the technique used here to determine indoor location, can achieve very good positioning accuracy even in classroom environments, where signal interference is usually very high. © 2016 IEEE.

Notes:

cited By 0; Conference of 8th IEEE International Conference on Technology for Education, T4E 2016 ; Conference Date: 2 December 2016 Through 4 December 2016; Conference Code:125986

Cite this Research Publication

S. Anand, Bijlani, K., Suresh, S., and Praphul, P., “Attendance Monitoring in Classroom Using Smartphone & Wi-Fi Fingerprinting”, in Proceedings - IEEE 8th International Conference on Technology for Education, T4E 2016, 2017, pp. 62-67.

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