Date of Publication :1st October 2022
Abstract: With the advent of computer technology, E-Learning has become an increasingly popular learning approach in higher Education institutions. Due to the outbreak of COVID-19 pandemic that demands substantial modification in the teaching-learning process across the globe. However, the student’s attendance management in virtual classroom is quite difficult. In this paper I introduce a smart intelligent virtual classroom, which include ML based face recognition system. Further, it can generate dedicated attendance reports, pinpointing students’ attention during virtual learning at random time intervals. Moreover, the novel random interval tracking system can also prevent the dropping out of participants from the virtual classroom. The distinctive feature of randomness in Random Interval Tracking System (RITS) ensures that student’s attention and engagement in virtual classroom are enhanced.
Reference :
-
- H. Yang and X. Han, "“Face recognition attendance system based on realtime video processing,‟ ", IEEE Access, vol. 8, pp. 159143–159150, 2020
- S. M. anzar,N.P.Subheesh , Alavikunju panthakan, Shanid malayil, and Hussain Al Ahmad,"“Random Interval Attendance Management System (RIAMS): A Novel Multimodal Approach for Post-COVID Virtual Learning" Received June 2, 2021, accepted June 18, 2021, date of publication June 24, 2021, date of current version July 1, 2021.DigitalObjectIdentifier10.1109/ACCESS.2021.309226 0
- Rachana Dinesh Kumar Bumb “Adaptive Learning Technique For Facial Recognition ” Masters Theses,2020
- R.Halder, R.Chatterjee,D.K.Sanyal, and p.k.Mallick, "Deep learningbased smart attendance monitoring system,”", in Proc. Global AI Congr. Singapore: Springer, 2020, pp. 101–115
- C. Rapanta, L.Botturi, P. Goodyear,L.Guardia,and M.koole, "Online university teaching during and after the Covid-19 crisis: Refocusing teacher presence and learning activity", Postdigital Sci. Educ., vol. 2, no. 3, pp. 923–945, Oct. 2020
- B. K. P. Mohamed and C. V. Raghu, and J. R. Goodall , “Fingerprint attendance system for classroom needs,”, in Proc. Annu. IEEE India Conf. (INDICON), Dec. 2012, pp. 433– 438.
- T. D. F. Pereira, J. Komulainen, A. Anjos, J. M. De Martino, A. Hadid, M. Pietikäinen, and S. Marcel,"“Face liveness detection using dynamic texture", EURASIP J. Image Video Process., vol. 2014, n
- N. Rekha and M. Kurian,"“Face detection in real time based on HOG,", Int. J. Adv. Res. Comput. Eng. Technol., vol. 3, no. 4, pp. 1345–1352, 2014. Dept of computer science and engineering MGM College of Engineering and Pharmaceutical Sciences Random Interval Tracking System for Virtual Learning 32.
- Y. Wen, K. Zhang, Z. Li, and Y. Qiao,"“A discriminative feature learning approach for deep face recognition,”" in Proc. Eur. Conf. Comput. Vis. Cham, Switzerland: Springer, 2016, pp. 499–515.
- K. Solanki and P. Pittalia, "“Review of face recognition techniques,”" Int. J. Comput. Appl., vol. 133, no. 12, pp. 20– 24, Jan. 2016.
- C. Ding and D. Tao, "“Trunk-branch ensemble convolutional neural networks for videobased face recognition,”" IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 4, pp. 1002– 1014, Apr. 2018.
- Y. Li, W. Song, and C. Cheng, "“Attendance system of face recognition based on raspberry pi” Microcontrollers Embedded Syst. Appl., vol. 16, no. 11, pp. 28–30, 34, 2016.
- E. Indra, M. Yasir, A. Andrian, D. Sitanggang, O. Sihombing, S. P. Tamba, and E. Sagala, "“Design and implementation of student attendance system based on face recognition by Haar-like features methods,” in Proc. 3rd Int. Conf. Mech., Electron., Comput., Ind. Technol. (MECnIT), Jun. 2020, pp. 336–342.
- S. Rahman, M. Rahman, and M. M. Rahman,"“„Automated student attendance system using fingerprint recognition” Edelweiss Appl. Sci. Technol., vol. 1, no. 2, pp. 90–94, Jan. 2018.
- U. A. Patel and S. Priya"“„Development of a student attendance management system using RFID and face recognition: A review” Int. J. Advance Res. Comput. Sci. Manage. Stud., vol. 2, no. 8, pp. 109–119, 2014.
- J. Ruan and J. Yin, "“Face detection based on facial features and linear support vector machines” in Proc. Int. Conf. Commun. Softw. Netw., 2009, pp. 371–375.
- Zhigangao, Yucaihuang, Leilei Zheng, Xiadong Li1, "„A Student Attendance Management Method Based on Crowdsensing in Classroom Environment „” Received February 5, 2021, accepted February 10, 2021, date of publication February 18, 2021, date of current version March 1,2021.DigitalObjectIdentifier10.1109/ACCESS.2021.30602 56.
- Valentin Bazarevsky, Yury Kartynnik, Andrey Vakunov, Karthik Raveendran, Matthias Grundmann, "“BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs” Google Research 1600 Amphitheatre Pkwy, Mountain View, CA 94043, USA.
- Sheng Chen1,2, Yang Liu2 , Xiang Gao2 , and Zhen Han, "“MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices” Research Institute, Watchdata Inc., Beijing, China sheng.chen, yang.liu.yj, xiang.gao@watchdata.com, zhan@bjtu.edu.cn.
- Florian Schrof,Dmitry Kalenichenko, James Philbin" “FaceNet: A Unified Embedding for Face Recognition and Clustering ” arXiv:1503.03832v3 [cs.CV] 17 Jun 2015 .
- Aneesa M P, Saabina N,Meera K"“Face Recognition using CNN:A Systematic Review ” Published in International Journal of Engineering Research Technology (IJERT) http://www.ijert.org ISSN: 2278-0181 IJERTV11IS060075, Vol. 11 Issue 06, June-2022