Online Attendance with Python Face Recognition and Django Framework

Myrna Dwi Rahmatya, Mochamad Fajar Wicaksono

Abstract


Online learning certainly requires an attendance system that is accessed anywhere with a minimum level of fraud. This research aimed to build an online attendance system using face recognition to prevent filling out online learning attendance represented by others. The online attendance system was built using the object-based system approach method. The system development method used was the waterfall. The development of this system utilizes the Django python framework, face recognition library, and OpenCV. This research delivered an attendance system that could not be represented by others. To record attendance, students visit the online attendance system. Students can only record attendance once according to the lecture schedule. The camera will capture the student’s face and equate it with the existing facial data. Only the registered student that his attendance data stored in the database. In addition, students cannot record attendance with face recognition outside of their lecture hours. This attendance system was tested using black-box testing. The test is carried out on the access button function to record attendance during the lecture schedules data and outside the lecture schedule, facial recognition function with valid and not valid facial data, function to store attendance data, and function to view attendance data recap in the current semester. The result showed that the attendance application with facial recognition is 100% running as it should and as expected.

Full Text:

PDF

References


R. A. Abumalloh et al., “The impact of coronavirus pandemic (COVID-19) on education: The role of virtual and remote laboratories in education,” Technol. Soc., vol. 67, p. 101728, Nov. 2021, doi: 10.1016/J.TECHSOC.2021.101728.

N. Iivari, S. Sharma, and L. Ventä-olkkonen, “International Journal of Information Management Digital transformation of everyday life – How COVID-19 pandemic transformed the basic education of the young generation and why information management research should care ?,” Int. J. Inf. Manage., vol. 55, p. 102183, 2020, doi: 10.1016/j.ijinfomgt.2020.102183.

U. Jayaraman, P. Gupta, S. Gupta, G. Arora, and K. Tiwari, “Recent development in face recognition,” Neurocomputing, vol. 408, pp. 231–245, 2020, doi: https://doi.org/10.1016/j.neucom.2019.08.110.

K. Mohammed, A. S. Tolba, and M. Elmogy, “Multimodal student attendance management system (MSAMS),” Ain Shams Eng. J., vol. 9, no. 4, pp. 2917–2929, Dec. 2018, doi: 10.1016/J.ASEJ.2018.08.002.

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: https://doi.org/10.1016/j.jksuci.2019.01.006.

S. Saraswathi, S. M, Y. Salini, and M. Venkatesh, “Student attendance system using bar code scanner,” in Materials Today: Proceedings, 2021, pp. 1–5, doi: https://doi.org/10.1016/j.matpr.2020.12.898.

S. Pradhan and V. Nanniyur, “Large scale quality transformation in hybrid development organizations – A case study,” J. Syst. Softw., vol. 171, p. 110836, 2021, doi: https://doi.org/10.1016/j.jss.2020.110836.

R. Wei, P. E. D. Love, W. Fang, H. Luo, and S. Xu, “Recognizing people’s identity in construction sites with computer vision: A spatial and temporal attention pooling network,” Adv. Eng. Informatics, vol. 42, p. 100981, 2019, doi: https://doi.org/10.1016/j.aei.2019.100981.

C. Yu and H. Pei, “Face recognition framework based on effective computing and adversarial neural network and its implementation in machine vision for social robots,” Comput. Electr. Eng., vol. 92, p. 107128, 2021, doi: https://doi.org/10.1016/j.compeleceng.2021.107128.

Y. Liu, H. Yu, C. Gong, and Y. Chen, “A real time expert system for anomaly detection of aerators based on computer vision and surveillance cameras,” J. Vis. Commun. Image Represent., vol. 68, p. 102767, 2020, doi: https://doi.org/10.1016/j.jvcir.2020.102767.

Y. Shi, Z. Zhang, K. Huang, W. Ma, and S. Tu, “Human-computer interaction based on face feature localization,” J. Vis. Commun. Image Represent., vol. 70, p. 102740, 2020, doi: https://doi.org/10.1016/j.jvcir.2019.102740.

Z. Zhu and Y. Cheng, “Application of attitude tracking algorithm for face recognition based on OpenCV in the intelligent door lock,” Comput. Commun., vol. 154, pp. 390–397, 2020, doi: https://doi.org/10.1016/j.comcom.2020.02.003.

M. You, X. Han, Y. Xu, and L. Li, “Systematic evaluation of deep face recognition methods,” Neurocomputing, vol. 388, pp. 144–156, 2020, doi: https://doi.org/10.1016/j.neucom.2020.01.023.

J. Liang, H. Tu, F. Liu, Q. Zhao, and A. K. Jain, “3D face reconstruction from mugshots: Application to arbitrary view face recognition,” Neurocomputing, vol. 410, pp. 12–27, 2020, doi: https://doi.org/10.1016/j.neucom.2020.05.076.

E. Planas and J. Cabot, “How are UML class diagrams built in practice? A usability study of two UML tools: Magicdraw and Papyrus,” Comput. Stand. Interfaces, vol. 67, p. 103363, 2020, doi: https://doi.org/10.1016/j.csi.2019.103363.

M. Ozkaya and F. Erata, “A survey on the practical use of UML for different software architecture viewpoints,” Inf. Softw. Technol., vol. 121, p. 106275, 2020, doi: https://doi.org/10.1016/j.infsof.2020.106275.

M. Abbas, R. Rioboo, C.-B. Ben-Yelles, and C. F. Snook, “Formal modeling and verification of UML Activity Diagrams (UAD) with FoCaLiZe,” J. Syst. Archit., vol. 114, p. 101911, 2021, doi: https://doi.org/10.1016/j.sysarc.2020.101911.

R. C. Waldemarin and C. R. G. de Farias, “OBO to UML: Support for the development of conceptual models in the biomedical domain,” J. Biomed. Inform., vol. 80, pp. 14–25, 2018, doi: https://doi.org/10.1016/j.jbi.2018.02.015.

M. Wang and W. Deng, “Deep face recognition: A survey,” Neurocomputing, vol. 429, pp. 215–244, 2021, doi: https://doi.org/10.1016/j.neucom.2020.10.081.

A. Pajaziti, X. Bajrami, and A. Paliqi, “Path Control of Quadruped Robot through Convolutional Neural Networks,” IFAC-PapersOnLine, vol. 51, no. 30, pp. 610–615, 2018, doi: https://doi.org/10.1016/j.ifacol.2018.11.222.

K. Yassin, J. Maher, M. Mehrez, and A. Mohamed, “Optical face detection and recognition system on low-end-low-cost Xilinx Zynq SoC,” Optik (Stuttg)., vol. 217, p. 164747, 2020, doi: https://doi.org/10.1016/j.ijleo.2020.164747.

I.-H. Chen, S.-C. Ho, and M.-B. Su, “Computer vision application programming for settlement monitoring in a drainage tunnel,” Autom. Constr., vol. 110, p. 103011, 2020, doi: https://doi.org/10.1016/j.autcon.2019.103011.

F. Tang et al., “An end-to-end face recognition method with alignment learning,” Optik (Stuttg)., vol. 205, p. 164238, 2020, doi: https://doi.org/10.1016/j.ijleo.2020.164238.

R. Deng et al., “Deep learning-based automatic detection of productive tillers in rice,” Comput. Electron. Agric., vol. 177, p. 105703, 2020, doi: https://doi.org/10.1016/j.compag.2020.105703.

A. Sleem, M. Abdel-Baset, and I. El-henawy, “PyIVNS: A python based tool for Interval-valued neutrosophic operations and normalization,” SoftwareX, vol. 12, pp. 100632 1–7, 2020, doi: https://doi.org/10.1016/j.softx.2020.100632.

G. M. Lunardi, G. M. Machado, V. Maran, and J. P. M. de Oliveira, “A metric for Filter Bubble measurement in recommender algorithms considering the news domain,” Appl. Soft Comput., vol. 97, p. 106771, 2020, doi: https://doi.org/10.1016/j.asoc.2020.106771.

K. Jaskolka, J. Seiler, F. Beyer, and A. Kaup, “A Python-based laboratory course for image and video signal processing on embedded systems,” Heliyon, vol. 5, no. 10, p. e02560 1-9, 2019, doi: https://doi.org/10.1016/j.heliyon.2019.e02560.

J. Zheng et al., “Django: Bilateral coflow scheduling with predictive concurrent connections,” J. Parallel Distrib. Comput., vol. 152, pp. 45–56, 2021, doi: https://doi.org/10.1016/j.jpdc.2021.01.006.

J. Granderson, G. Lin, D. Blum, J. Page, M. Spears, and M. A. Piette, “Integrating diagnostics and model-based optimization,” Energy Build., vol. 182, pp. 187–195, 2019, doi: https://doi.org/10.1016/j.enbuild.2018.10.015.

A. A. Almeida de Macedo Oliveira and R. Hirata, “INACITY - Investigate and Analyze a CITY,” SoftwareX, vol. 15, pp. 100777 1–8, 2021, doi: https://doi.org/10.1016/j.softx.2021.100777.




DOI: https://doi.org/10.32520/stmsi.v12i3.2773

Article Metrics

Abstract view : 582 times
PDF - 197 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.