Development of Automated Attendance System Using Pretrained Deep Learning Models

Main Article Content

Muhammad Shahrul Zaim Ahmad
Nor Azlina Ab. Aziz
Anith Khairunnisa Ghazali

Abstract

Abstract - Smart classroom enables better learning experience to the students and aid towards efficient campus' management. Many studies have shown positive correlation between attendance and student's performance, where the higher the attendance, the better the student's performance. Therefore, many higher learning institutions make class attendance compulsory and students' attendance are recorded. Technological solutions for an advanced attendance system such as face recognition is highly desirable. The authenticity of attendance can be ensured by using such solution. In this work, artificial intelligence based face recognition system is used for attendance recording system. The recognized face is used to confirm the presence of a student to the class. Six pretrained face recognition model are evaluated for the adoption in the system developed. The FaceNet, is adopted in this work with accuracy of more than 95%. The automation system is supported by IoT.


[Manuscript received: 1 July 2023 | Accepted: 12 December 2023 | Published: 30 April 2024]

Article Details

How to Cite
Ahmad, M. S. Z. ., Ab. Aziz, N. A. ., & Ghazali, A. K. . (2024). Development of Automated Attendance System Using Pretrained Deep Learning Models . International Journal on Robotics, Automation and Sciences, 6(1), 6–12. https://doi.org/10.33093/ijoras.2024.6.1.2
Section
Articles

References

R. Ani, S. Krishna, H. Akhil, and U. Arun, “An Approach Towards Building an IoT Based Smart Classroom,” 2018 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2018, pp. 2098–2102, 2018, doi: 10.1109/ICACCI.2018.8554869.

A. Gupta, P. Gupta, and J. Chhabra, “loT based Power Efficient System Design using Automation for Classrooms,” 2015 Third Int. Conf. Image Inf. Process. loT, pp. 285–289, 2015.

A. Nyamapfene, “Does class attendance still matter?,” Eng. Educ., vol. 5, no. 1, pp. 64–74, 2010, doi: 10.11120/ened.2010.05010064.

D. Hendrycks, K. Lee, and M. Mazeika, “Using pre-training can improve model robustness and uncertainty,” 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019-June, no. 2018, pp. 4815–4826, 2019.

L. D’cruz and J. Harirajkumar, “WITHDRAWN: Contactless attendance system using Siamese neural network based face recognition,” Mater. Today Proc., no. xxxx, 2020, doi: 10.1016/j.matpr.2020.10.462.

D. Sunaryono, J. Siswantoro, and R. Anggoro, “An android based course attendance system using face recognition,” J. King Saud Univ. - Comput. Inf. Sci., vol. 33, no. 3, pp. 304–312, 2021, doi: 10.1016/j.jksuci.2019.01.006.

V. Seelam, A. K. Penugonda, B. Pavan Kalyan, M. Bindu Priya, and M. Durga Prakash, “Smart attendance using deep learning and computer vision,” Mater. Today Proc., vol. 46, no. xxxx, pp. 4091–4094, 2020, doi: 10.1016/j.matpr.2021.02.625.

B. D. Kumar, H. A. Mir, M. K. Ahmed, and M. T. Siddiqui, “Exam form automation using facial recognition,” Mater. Today Proc., vol. 80, no. xxxx, pp. 2236–2240, 2023, doi: 10.1016/j.matpr.2021.06.190.

S. M. Bah and F. Ming, “An improved face recognition algorithm and its application in attendance management system,” Array, vol. 5, no. February 2019, p. 100014, 2020, doi: 10.1016/j.array.2019.100014.

Mayur S., Priya J., Sujata J., & Minakshi V.., “Automatic Attendance System using Face Recognition Technique,” Int. J. Recent Technol. Eng., vol. 9, no. 1, pp. 2134–2138, 2020, doi: 10.35940/ijrte.a2644.059120.

Mardiana, M. A. Muhammad, and Y. Mulyani, “Library Attendance System using YOLOv5 Faces Recognition,” Proc. - ICCTEIE 2021 2021 Int. Conf. Converging Technol. Electr. Inf. Eng. Converging Technol. Sustain. Soc., pp. 68–72, 2021, doi: 10.1109/ICCTEIE54047.2021.9650628.

S. Chintalapati and M. V. Raghunadh, “Automated attendance management system based on face recognition algorithms,” 2013 IEEE Int. Conf. Comput. Intell. Comput. Res. IEEE ICCIC 2013, pp. 1–5, 2013, doi: 10.1109/ICCIC.2013.6724266.

F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June-2015, pp. 815–823, 2015, doi: 10.1109/CVPR.2015.7298682.

M. S. Brandon Amos, Bartosz Ludwiczuk, “Openface: uniwersalna biblioteka rozpoznawania twarzy z aplikacjami mobilnymi,” C. Sch. ..., 2016 r., vol. 16, no. October 2015, pp. 1–18, 2016, [Online]. Available: http://reports-archive.adm.cs.cmu.edu/anon/anon/usr0/ftp/2016/CMU-CS-16-118.pdf

Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “DeepFace: Closing the gap to human-level performance in face verification,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 1701–1708, 2014, doi: 10.1109/CVPR.2014.220.

J. Deng, J. Guo, J. Yang, N. Xue, I. Kotsia, and S. Zafeiriou, “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 10, pp. 5962–5979, 2022, doi: 10.1109/TPAMI.2021.3087709.

Y. Sun, X. Wang, and X. Tang, “Deep learning face representation from predicting 10,000 classes,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 1891–1898, 2014, doi: 10.1109/CVPR.2014.244.

S. I. Serengil and A. Ozpinar, “LightFace: A Hybrid Deep Face Recognition Framework,” Proc. - 2020 Innov. Intell. Syst. Appl. Conf. ASYU 2020, 2020, doi: 10.1109/ASYU50717.2020.9259802.

L. J. Seong, S. Yogarayan, S. F. A. Razak and A. Azman, "Face Recognition and Physiological Signal for Impaired Drivers: A Review," 2023 11th International Conference on Information and Communication Technology (ICoICT), Melaka, Malaysia, 2023, pp. 582-587, doi: 10.1109/ICoICT58202.2023.10262615.

Jia Rou, Lee & Ng, K.-W & Yoong, Yih-Jian. (2023). Face and Facial Expressions Recognition System for Blind People Using ResNet50 Architecture and CNN. Journal of Informatics and Web Engineering. 2. 284-298. 10.33093/jiwe.2023.2.2.20.