Automating the Attendance System by Face Recognition in Rural Schools Reducing Time and Errors also Making Record Remotly Available
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Abstract
First thing every teacher typically does when they get to school is taken attendance for their first class of the day, and this is still done on a piece of paper, completely manually, and takes a large amount of time for the teacher to decide if a student is present or not. Furthermore, many rural schools continue to use these old ways of taking attendance (In paper Register). As we all know, these types of methods are slow, they can be easily messed up, and therefore very difficult to manage.
This research paper provides a method to take attendance automatically using a system developed for rural school systems. Our method can be implemented by any rural school, even those who don't currently have any advanced teaching support or technology. Our system will require cameras to monitor students in classrooms, taking live video of the students and processing this video to identify which students are in class Room based on their identity in the school's database, and automatically updating the database of student attendance.
Implementing this method will eliminate the need for manually taking attendance of classes and save time for both teachers and students. This method will also reduce paper work and will have accurate and more secure records of student attendance maintained for many years. This research paper will provide a solution for the Automatic Attendance System at a very low cost to rural school.
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References
[1] N. Soni, M Kumar and G. Mathur, “Face Recognition using SOM Nural Network with Different Facial Feature Extraction Techniques, “International Journal of computer pplications, vol 76, no.3, pp 7-11, 2013.
[2] Md. Sajid Akbar, Abu Musa AI Ashray, Pronob Saker, Jia Uddin, Ahmad Tamim Mansoor. Face Recognition and RFID Verified Attendance System. International Conference on Computer and Information Technology (ICCIT). 2018; 21(4):42-47
[3] Abin Abraham, Mehul Bapse, Yash Kalaria, Ahmer Usmani. Face Recognition Base Attendance System. International Journal of Engineering Research & Technology (IJERT). 2020; 9940:200-205.
[4] Abshish Jadhav, Dhwaniket Kamble, Santosh B. Rathod, Sumita Kuamr, Pratima Kadam, Mohammad Dalwai. Attendance Management System Using Face Recognition. International Journal of Emerging Technologies and Innovative Research (JETIR). 2034;11(2):45-50.
[5] Li, Q., Zhang, Y., & Chen, T. (2021). Facial recognition-based automatic attendance system using convolutional neural networks. International Journal of Intelligent Systems, 36(3), 1104-1115.
[6] Patel, R., & Kumar, S. (2020). Face detection using deep learning algorithms: A review. International Journal of Computer Applications, 182(7), 15-22.
[7] Jain, P., Gupta, M., & Jain, S. (2020). Automatic Attendance System Using Face Recognition . International Journal of Advanced Computer Science and Applications, 11(6), 107-113.
[8] Mariyam Mahboob, Nounamn Mohsin Abdul Qadir. Attendance Management System Using Face Recognition. International Research Journal of Modernization in Engineering Technology and Science (IRJMETS). 2024; 6(4): 122-128.
[9] Samridhi Dev, Tushar Patnaik. Student Attendance System Using Face Recognition. International Journal of Scientific & Technology Research (IJSTR). 2020,9(3): 4402-4405.
[10] Sharma, S., & Patel, V. (2021). Comparative study of face detection algorithms in automatic attendance systems. Journal of Machine Learning & AI Research, 12(1), 25-32.
[11] W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: A literature survey,” ACM Computing Surveys, vol. 35, no. 4, pp. 399–458, 2003.
[12] P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2001, pp. 511–518.
[13] F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 815–823.
[14] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
[15] O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep face recognition,” in Proc. British Machine Vision Conference (BMVC), 2015, pp. 1–12.
[16] Maurya, Vijendra Kumar, et al. "Adopting blockchain technology for smart farming and food security." International Conference on Data Science and Applications. Singapore: Springer Nature Singapore, 2023.