Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact

Authors

DOI:

https://doi.org/10.34306/att.v6i3.497

Keywords:

Smart Attendance System, Facial Recognition, ESP32-S3, Raspberry Pi 5, Internet of Things

Abstract

Current attendance methods, though simple, are prone to manipulation and can be time consuming for both educators and students. For instance, manual systems and QR code based methods allow students to register attendance on behalf of others due to the lack of unique identification. While calling names individually improves security, it disrupts the learning process by consuming significant time. This study addresses these issues by developing an autonomous robot, CObot, equipped with a facial recognition system powered by a Raspberry Pi microcontroller. CObot navigates classrooms autonomously, avoiding obstacles, and efficiently records attendance without requiring movement from students or educators. The use of facial recognition ensures that only registered individuals can mark attendance, creating a secure and tamper-proof system. Additionally, the integration of Internet of Things (IoT) technology enables real-time data transfer to Google Sheets, simplifying record-keeping and reducing educators administrative workload. A 3D-printed, customizable car structure enhances the robot design, while the Raspberry Pi 5 was selected over alternatives like the ESP32-S3 for its superior processing power and faster data transfer speeds, ensuring smoother operations. In testing with 60 participants, the Raspberry Pi 5 demonstrated a 99% accuracy rate in facial recognition, outperforming the ESP32-S3 90% accuracy. By saving time, improving security, and reducing manual effort, CObot enhances the classroom environment, benefiting both students and educators. While the improvement in attendance systems may appear incremental, CObot represents a meaningful step toward fostering a more efficient and effective learning environment.

References

N. Nordin and N. H. M. Fauzi, “A web-based mobile attendance system with facial recognition feature,” International Journal of Interactive Mobile Technologies (iJIM), vol. 14, no. 05, p. 193, April 2020.

A. S. Alon, “A yolov3 inference approach for student attendance face recognition system,” International Journal of Emerging Trends in Engineering Research (IJETER), vol. 8, no. 2, pp. 384–390, February 2020.

S. C. Hoo and H. Ibrahim, “Biometric-based attendance tracking system for education sectors: A literature survey on hardware requirements,” Journal of Sensors, vol. 2019, pp. 1–25, September 2019.

A. Puckdeevongs, N. K. Tripathi, A. Witayangkurn, and P. Saengudomlert, “Classroom attendance systems based on bluetooth low energy indoor positioning technology for smart campus,” Information, vol. 11, no. 6, p. 329, June 2020.

M. Zhao, G. Zhao, and M. Qu, “College smart classroom attendance management system based on internet of things,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–9, July 2022.

S. N. Shah and A. Abuzneid, “Iot based smart attendance system (sas) using rfid,” in 2019 IEEE Long Island Systems, Applications and Technology Conference (LISAT). IEEE, May 2019, pp. 1–6.

H. E. Mrabet and A. A. Moussa, “Iot-school attendance system using rfid technology,” International Journal of Interactive Mobile Technologies (iJIM), vol. 14, no. 14, p. 95, August 2020.

D. S. Radhakrishnan, “Machine learning biometric system using viola jones algorithm,” International Journal for Research in Applied Science and Engineering Technology (IJRASET), vol. 11, no. 5, pp. 5209–5211, May 2023.

K. Sanath, K. Meenakshi, M. Rajan, V. Balamurugan, and M. Harikumar, “Rfid and face recognition based smart attendance system,” in 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2021, pp. 492–499.

A. A. Z. et al., “Integration of car rental system with mobile app management and iot for optimised resources and safety in iium,” in THE 1ST INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING, SYSTEMS, AND APPLICATIONS (InCASA) 2023. AIP Publishing, 2024, p. 030001.

A. A. Zainuddin, R. M. Nor, A. A. A. Hussin, and M. N. M. Sazali, “Mqtt-enabled smart door access system: Design and implementation using nodemcu esp 8266 and hivemq,” in 2023 IEEE 9th International Conference on Computing, Engineering and Design (ICCED). Kuala Lumpur, Malaysia: IEEE, Nov 2023, pp. 1–6.

A. A. Zainuddin, N. F. Omar, N. N. Zakaria, and N. A. M. Camara, “Privacy-preserving techniques for iot data in 6g networks with blockchain integration: A review,” IJPCC, vol. 9, no. 2, pp. 80–92, Jul 2023.

A. A. Zainuddin et al., “Recent trends of integration of blockchain technology with the iot by analysing the networking systems: Future research prospects,” JKMP, vol. 23, no. 1, Dec 2023.

A. A. B. A. Hussin, H. S. B. H. Muzammil, M. A. A. B. Shaharuddin, A. R. B. Ismail, and A. A. B. Zainuddin, “Utilizing different edge detection and preprocessing techniques to improve the accuracy of durian cultivar detection using convolutional neural networks,” in 2023 IEEE 9th International Conference on Computing, Engineering and Design (ICCED). IEEE, November 2023, pp. 1–6.

Badan Perencanaan Pembangunan Nasional (Bappenas), “Tujuan pembangunan berkelanjutan (sustainable development goals),” 2024, accessed: 2024-11-07. [Online]. Available: https: //sdgs.bappenas.go.id/

D. D. Nguyen, X. H. Nguyen, T. T. Than, and M. S. Nguyen, “Automated attendance system in the classroom using artificial intelligence and internet of things technology,” in 2021 8th NAFOSTED Conference on Information and Computer Science (NICS). IEEE, 2021, pp. 531–536.

P. D. Mishra, “A comparative study of face recognition models for smart attendance,” IJRASET, vol. 11, no. 5, pp. 6666–6670, May 2023.

A. Kumar, V. Indragandhi, A. Chitra, R. PauL, and S. Banerjee, “Smart attendance management system using raspberry pi and deep learning technique,” in Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India, 2021.

U. R. Ughade, S. M. Gikwad, A. N. Yeole, and D. A. O. Mulani, “Automatic attendance system using face recognition,” JIPIRS, no. 34, pp. 11–18, Jul 2023.

M. H. R. Chakim, S.-C. Chen, C. Nas, R. Supriati, and G. P. Cesna, “Integration of iot and blockchain technologies for enhancing transparency and efficiency in indonesian agriculture,” in 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT). IEEE, 2024, pp. 1–6.

M. M. Sari, S. Pranata, V. D. Sulaiman et al., “Innovative economic development in developing countries through ai and tackling globalization,” in 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT). IEEE, 2024, pp. 1–6.

D. P. Hausherr and D. Berben, “(all-in-one) power supply system for mobile and network-wired raspberry pi-based internet of things applications,” Hardware, vol. 1, no. 1, pp. 54–69, Dec 2023.

F. Azmi, A. Saleh, and A. Ridwan, “Smart management attendance system with facial recognition using computer vision techniques on the raspberry pi,” IJIRCST, vol. 11, no. 1, pp. 38–44, Jan 2023.

K. Alhanaee, M. Alhammadi, N. Almenhali, and M. Shatnawi, “Face recognition smart attendance system using deep transfer learning,” Procedia Computer Science, vol. 192, pp. 4093–4102, 2021.

S. Sawhney, K. Kacker, S. Jain, S. N. Singh, and R. Garg, “Real-time smart attendance system using face recognition techniques,” in 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). Noida, India: IEEE, Jan 2019, pp. 522–525.

T.-V. Dang, “Smart attendance system based on improved facial recognition,” Journal of Robotics and Control, vol. 4, no. 1, pp. 46–53, Feb 2023.

A. Yadav et al., “Smart attendance system using face recognition,” SSRN Journal, 2024.

N. Rathour et al., “Iomt based facial emotion recognition system using deep convolution neural networks,” Electronics, vol. 10, no. 11, p. 1289, May 2021.

W. Zhang, H. Liu, X. Zhang, X. Li, G. Zhang, and P. Cao, “3d printed micro-electrochemical energy storage devices: From design to integration,” Adv Funct Materials, vol. 31, no. 40, p. 2104909, Oct 2021.

Q. Chen, J. Zhao, J. Ren, L. Rong, P. Cao, and R. C. Advincula, “3d printed multifunctional, hyperelastic silicone rubber foam,” Adv Funct Materials, vol. 29, no. 23, p. 1900469, Jun 2019.

P. M. Cogswell, M. A. Rischall, A. E. Alexander, H. J. Dickens, G. Lanzino, and J. M. Morris, “Intracranial vasculature 3d printing: review of techniques and manufacturing processes to inform clinical practice,” 3D Print Med, vol. 6, no. 1, p. 18, Dec 2020.

M. Tavafoghi et al., “Multimaterial bioprinting and combination of processing techniques towards the fabrication of biomimetic tissues and organs,” Biofabrication, vol. 13, no. 4, p. 042002, Oct 2021.

C. Li, F. Bu, Q. Wang, and X. Liu, “Recent developments of inkjet-printed flexible energy storage devices,” Adv Materials Inter, vol. 9, no. 34, p. 2201051, Dec 2022.

Downloads

Published

2024-12-11

How to Cite

Zainuddin, A. A., Nor, R. M., Handayani, D., Tamrin, M. I. M., Subramaniam, K., & Sadikan, S. F. N. (2024). Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact. Aptisi Transactions on Technopreneurship (ATT), 6(3), 608–622. https://doi.org/10.34306/att.v6i3.497

Issue

Section

Articles