Autonomous Attendance System Using Facial Recognition with User Friendly GUI

Authors

  • Swathika K K  B. Tech Department of Information Technology, Sri Shakthi Institute of Engineering and Technology (Autonomous Institution), Coimbatore, Tamil Nadu, India.
  • Tharunika K  B. Tech Department of Information Technology, Sri Shakthi Institute of Engineering and Technology (Autonomous Institution), Coimbatore, Tamil Nadu, India.
  • Sabari Swetha D  B. Tech Department of Information Technology, Sri Shakthi Institute of Engineering and Technology (Autonomous Institution), Coimbatore, Tamil Nadu, India.
  • Dr. S. Prakash  Professor, Sri Shakthi Institute of Engineering and Technology (Autonomous Institution), Coimbatore, Tamil Nadu, India.
  • Dr. R. P. S. Manikandan  Associate Professor, Sri Shakthi Institute of Engineering and Technology (Autonomous Institution), Coimbatore, Tamil Nadu, India.
  • Mr. B. Varunkumar  Assistant Professor, Sri Shakthi Institute of Engineering and Technology (Autonomous Institution), Coimbatore, Tamil Nadu, India.

DOI:

https://doi.org//10.32628/CSEIT228142

Keywords:

Attendance, Automation, Fingerprint, RFID, Facial detection, accuracy, dataset, classroom

Abstract

One of the routine processes which is common in every educational institution is marking attendance for students. People use different ways to mark attendance for students like calling their name aloud and receiving their voice output to mark the attendance. And it is time consuming process and there may be chance for mistakes. There are many ways to make this process into automation like Fingerprint based approach, RFID, Iris and facial detection and recognition. In this work, we will see about one of best and effective approach to solve this problem using machine learning process of facial detection and recognition. Initially the pictures of students will be collected and trained under different lighting conditions and nominal accuracy can be achieved. Then the model will be tested with trained datasets and this process continues until the method works well in real environment(classroom).

References

  1. Balcoh, Naveed Khan, M. Haroon Yousaf, Waqar Ahmad, and M. Iram Baig. "Algorithm for efficient attendance management: Face recognition based approach." International Journal of Computer Science Issues (IJCSI) 9, no. 4 (2012): 146.
  2. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001, pp. I-I, doi: 10.1109/CVPR.2001.990517.
  3. Suma, S. L., and Sarika Raga. "Real time face recognition of human faces by using LBPH and Viola Jones algorithm." International Journal of Scientific Research in Computer Science and Engineering 6, no. 5 (2018): 6-10.
  4. Meier, Burkhard. Python GUI Programming Cookbook: Develop functional and responsive user interfaces with tkinter and PyQt5. Packt Publishing Ltd, 2019.
  5. Hunt, John. "Working with Excel Files." In Advanced Guide to Python 3 Programming, pp. 249-255. Springer, Cham, 2019.
  6. Jha, Abhishek. "Class room attendance system using facial recognition system." The International journal of Mathematics, science, technology and Management 2, no. 3 (2007): 4-7.
  7. Siswanto, Adrian Rhesa Septian, Anto Satriyo Nugroho, and Maulahikmah Galinium. "Implementation of face recognition algorithm for biometrics based time attendance system." In 2014 International Conference on ICT For Smart Society (ICISS), pp. 149-154. IEEE, 2014.

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Published

2022-02-28

Issue

Section

Research Articles

How to Cite

[1]
Swathika K K, Tharunika K, Sabari Swetha D, Dr. S. Prakash, Dr. R. P. S. Manikandan, Mr. B. Varunkumar, " Autonomous Attendance System Using Facial Recognition with User Friendly GUI, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 1, pp.256-260, January-February-2022. Available at doi : https://doi.org/10.32628/CSEIT228142