CFP last date
20 January 2025
Reseach Article

Implementation of Attendance System using Face Recognition and PCA

by Sonali Patil, Avdhoot Gaikwad, Chetana Baviskar, Shrawani Bartakke, Shubham Kulkarni
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 50
Year of Publication: 2022
Authors: Sonali Patil, Avdhoot Gaikwad, Chetana Baviskar, Shrawani Bartakke, Shubham Kulkarni
10.5120/ijca2022921911

Sonali Patil, Avdhoot Gaikwad, Chetana Baviskar, Shrawani Bartakke, Shubham Kulkarni . Implementation of Attendance System using Face Recognition and PCA. International Journal of Computer Applications. 183, 50 ( Feb 2022), 54-57. DOI=10.5120/ijca2022921911

@article{ 10.5120/ijca2022921911,
author = { Sonali Patil, Avdhoot Gaikwad, Chetana Baviskar, Shrawani Bartakke, Shubham Kulkarni },
title = { Implementation of Attendance System using Face Recognition and PCA },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2022 },
volume = { 183 },
number = { 50 },
month = { Feb },
year = { 2022 },
issn = { 0975-8887 },
pages = { 54-57 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number50/32269-2022921911/ },
doi = { 10.5120/ijca2022921911 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:26.931788+05:30
%A Sonali Patil
%A Avdhoot Gaikwad
%A Chetana Baviskar
%A Shrawani Bartakke
%A Shubham Kulkarni
%T Implementation of Attendance System using Face Recognition and PCA
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 50
%P 54-57
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The prototype of an automated Online Biometric-enabled Class Attendance Register System is presented in this study (OBCARS). The technology is being planned and developed to address the problem of lost and/or shredded attendance record paper sheets in higher education classrooms. The system also seeks to provide a reliable and efficient class attendance tracking system that prevents students from imitating attendance markers and streamlines the calculation of students' attendance records. Both pragmatic biometric behaviourin contrast to previously poised data for a focus and an open-mindedness of computation are included in biometric appreciation. Estimated identical is required because to variances in biological features and deeds both within and among humans. It determines a student's attendance by their attendance in class. The technology willrecognize the student's face and save the response to the database automatically.

References
  1. K. R. Pireva, J. Siqeca, and S. Berisha, “RFID: Management system for students’ attendance,” in IFAC Proceedings Volumes (IFACPapersOnline), 2013, vol. 15, no. PART 1, pp. 137–140.
  2. R. Lodha, S. Gupta, H. Jain, and H. Narula, “Bluetooth Smart based attendance management system,” in Procedia Computer Science, 2015, vol. 45, no. C, pp. 524–527.
  3. N. D. Veer and B. F. Momin, “An automated attendance system using video surveillance camera,” in 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2016.
  4. I. L. Ruiz and M. Á. Gómez-Nieto, “Combining of NFC, BLE and Physical Web Technologies for Objects Authentication on IoT Scenarios,” in Procedia Computer Science, 2017, vol. 109, pp. 265– 272.
  5. 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.
  6. J. Qin, X. J. Shen, M. Zou, and S. P. Qin, “An Automotive Needle Meter Dynamic Test Method Based on Computer Vision and HIL Technology,” in Procedia Computer Science, 2018, vol. 154, pp. 588–595.
  7. J. Zhang, L. He, M. Karkee, Q. Zhang, X. Zhang, and Z. Gao,\ “Branch detection for apple trees trained in fruiting wall architecture using depth features and Regions-Convolutional Neural Network (R- CNN),” Comput. Electron. Agric., vol. 155, pp. 386–393, Dec. 2018.
  8. X. Liu, H. A. Ounifi, A. Gherbi, Y. Lemieux, and W. Li, “A hybrid GPU-FPGA-based computing platform for machine learning,” in Procedia Computer Science, 2018, vol. 141, pp. 104–111.
  9. D. Sunaryono, J. Siswantoro, and R. Anggoro, “An android based course attendance system using face recognition,” J. King Saud Univ. - Comput. Inf. Sci., 2019.
  10. M. SyaifulRomadhon, A. Rahmah, and Y. Wirani, “Blended learning system using social media for college student: A case of tahsin education,” in Procedia Computer Science, 2019, vol. 161, pp. 160–167.
  11. S. V. Shavetov, I. I. Merkulova, A. A. Ekimenko, O. I. Borisov, and V. S. Gromov, “Computer Vision in Control and Robotics for\ Educational Purposes,” in IFAC-PapersOnLine, 2019, vol. 52, no. 9, pp. 144–146.
  12. de Vicente Mohino, Bermejo Higuera, Bermejo Higuera, and Sicilia Montalvo, “The Application of a New Secure Software Development Life Cycle (S-SDLC) with Agile Methodologies,” electronics, vol. 8, no. 11, p. 1218, Oct. 2019.
  13. A. Winkler-Schwartz et al., “Artificial Intelligence in Medical\ Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation,” J. Surg. Educ., vol. 76, no. 6, pp. 1681–1690, Nov. 2019.
  14. G. Guo and N. Zhang, “A survey on deep learning based face recognition,” Comput. Vis. Image Underst., vol. 189, Dec. 2019.
  15. WenxianZeng, QinglinMeng, Ran Li “Design of Intelligent Classroom Attendance System Based on Face Recognition” 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2019)
  16. A. Elmahmudi and H. Ugail, “Deep face recognition using imperfect facial data,” Futur.Gener.Comput. Syst., vol. 99, pp. 213–225, Oct. 2019.
  17. L. K. Almajmaie, O. N. Ucan, and O. Bayat, “Fingerprint recognition system based on modified multi-connect architecture (MMCA),” Cogn. Syst. Res., vol. 58, pp. 107–113, Dec. 2019.
  18. S. M. Bah and F. Ming, “An improved face recognition algorithm and its application in attendance management system,” Array, vol. 5, p. 100014, Mar. 2020.
  19. U. Jayaraman, P. Gupta, S. Gupta, G. Arora, and K. Tiwari, “Recent development in face recognition,” Neuro computing, 2020.
  20. S. Afra and R. Alhajj, “Early warning system: From face recognition by surveillance cameras to social media analysis to detecting suspicious people,” Phys. A Stat. Mech. its Appl., vol. 540, p. 123151, Feb. 2020.
  21. I. Jegham, A. Ben Khalifa, I. Alouani, and M. A. Mahjoub, “Visionbased human action recognition: An overview and real world challenges,” Forensic Sci. Int. Digit. Investig., vol. 32, p. 200901, Mar. 2020.
  22. D. X. Zhou, “Theory of deep convolutional neural networks: Down sampling,” Neural Networks, vol. 124, pp. 319–327, Apr. 2020.
  23. M. You, X. Han, Y. Xu, and L. Li, “Systematic evaluation of deep face recognition methods,” Neuro computing, vol. 388, pp. 144–156, May 2020.
  24. A. Khatami, A. Nazari, A. Khosravi, C. P. Lim, and S. Nahavandi, “A weight perturbation-based regularisation technique for convolutional neural networks and the application in medical imaging,” Expert Syst. Appl., vol. 149, Jul. 2020.
  25. R. Taufiq, M. Baharun, B. Sunaryo, B. Pudjoatmodjo, and W. M. Utomo, “Indonesia: Covid-19 and E-Learning in Student Attendance Method,” SciTech Framew., vol. 2, no. 1, pp. 12–22, 2020.
  26. SonaliAppasahebPatil, L. Arun Raj, Bhupesh Kumar Singh, "Prediction of IoT Traffic Using the Gated Recurrent Unit Neural Network- (GRU-NN-) Based Predictive Model", Security and Communication Networks, vol. 2021, Article ID 1425732, 7 pages, 2021.
  27. S. Patil and L. A. Raj, “Classification of traffic over collaborative IoT and Cloud platforms using deep learning recurrent LSTM”, csci, vol. 22, no. 3, Sep. 2021.
Index Terms

Computer Science
Information Sciences

Keywords

Face Recognition Attendance System Python Open CV PCA Deep Learning Image Processing Database.