Face Recognition Attendance System Using Local Binary Pattern Histogram Algorithm

Year : 2026 | Volume : 13 | Issue : 01 | Page : 29 34
    By

    Yash Rawat,

  • Harsh Tongar,

  • Hemant,

  1. Student, Department of Computer Science and Engineering, Echelon Institute of Technology, Faridabad, Haryana, India
  2. Student, Department of Computer Science and Engineering, Echelon Institute of Technology, Faridabad, Haryana, India
  3. Student, Department of Computer Science and Engineering, Echelon Institute of Technology, Faridabad, Haryana, India

Abstract

Maintaining accurate and tamper-proof attendance records in educational and corporate environments has long been a challenge due to the limitations of manual and biometric systems. This study introduces the development and deployment of a contactless, automated attendance system that utilizes facial recognition through the local binary pattern histogram (LBPH) algorithm. The primary goal is to offer a secure and efficient substitute for conventional attendance methods by harnessing the power of computer vision and machine learning technologies. The system is developed using Python and OpenCV and employs Haar cascade classifiers for face detection, followed by the LBPH algorithm for facial recognition. A graphical user interface (GUI) is integrated using Tkinter to manage enrollment, training, and real-time attendance logging. The system securely stores attendance records in a MySQL database, with unrecognized faces being identified and managed accordingly. It is designed to be lightweight, functioning efficiently without the need for graphics processing unit (GPU) acceleration, and performs well on typical desktop computers. The system was tested under various lighting conditions and facial changes, including the presence of glasses and facial hair. The system achieved an average recognition accuracy of approximately 90% and was able to correctly reject unregistered faces, minimizing false positives. Real-time response and high accuracy make this system suitable for institutional deployment. This project demonstrates how a well-trained facial recognition model, when paired with a simple user interface and optimized backend, can significantly improve attendance tracking. The solution is scalable, cost-effective, and provides a foundation for further enhancements such as cloud integration, anti-spoofing, and mobile platform support.

Keywords: Face recognition, attendance system, local binary pattern histogram (LBPH), Haar cascade, automation

[This article belongs to Journal of Image Processing & Pattern Recognition Progress ]

How to cite this article:
Yash Rawat, Harsh Tongar, Hemant. Face Recognition Attendance System Using Local Binary Pattern Histogram Algorithm. Journal of Image Processing & Pattern Recognition Progress. 2026; 13(01):29-34.
How to cite this URL:
Yash Rawat, Harsh Tongar, Hemant. Face Recognition Attendance System Using Local Binary Pattern Histogram Algorithm. Journal of Image Processing & Pattern Recognition Progress. 2026; 13(01):29-34. Available from: https://journals.stmjournals.com/joipprp/article=2026/view=240042


References

  1. Joseph J, Zacharia KP. Automatic attendance management system using face recognition. Int J Sci Eng Res. 2013;2(11):327–330.
  2. Ononiwu GC, Okorafor GN. Radio frequency identification (RFID) based attendance system with automatic door unit. Acad Res Int. 2012 Mar;2(2):168–181.
  3. Shoewu O, Idowu OA. Development of attendance management system using biometrics. Pac J Sci Technol. 2012;13(1):300–307.
  4. Turk M, Pentland A. Eigenfaces for recognition. J Cogn Neurosci. 1991;3(1):71–86. doi:10.1162/jocn.1991.3.1.71.
  5. Belhumeur PN, Hespanha JP, Kriegman DJ. Eigenfaces vs. fisherfaces: Recognition using class- specific linear projection. IEEE Trans Pattern Anal Mach Intell. 1997;19(7):711–720. doi:10.1109/34.598228.
  6. Ahonen T, Hadid A, Pietikäinen M. Face description with local binary patterns: Application to face recognition. IEEE Trans Pattern Anal Mach Intell. 2006;28(12):2037–2041. doi:10.1109/TPAMI.2006.244.
  7. Viola P, Jones MJ. Robust real-time face detection. Int J Comput Vis. 2004;57(2):137–154. doi:10.1023/B:VISI.0000013087.49260.fb.
  8. Salton K. (2017). Face recognition: understanding LBPH algorithm. [Online] Medium. TDS Archive. Available from: https://medium.com/data-science/face-recognition-how-lbph-works- 90ec258c3d6b
  9. Dubey SR, Singh SK, Chaudhuri BB. Activation functions in deep learning: A comprehensive survey and benchmark. Neurocomputing. 2022;503:92–108. doi:10.1016/j.neucom.2022.06.111.
  10. Ahmed A, Guo J, Ali F, Deeba F. LBPH based improved face recognition at low resolution. 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China. 2018. p. 144–147. doi: 10.1109/ICAIBD.2018.8396183.
  11. Ariffin NABM, Gimba UA, Musa A. Face detection based on Haar cascade and convolution neural network (CNN). J Adv Res Comput Appl. 2025;38(1):1–11.
  12. Singhal N, Ganganwar V, Yadav M, Chauhan A, Jakhar M, Sharma K. Comparative study of machine learning and deep learning algorithm for face recognition. Jordanian J Comput Inf Technol. 2021;7:1. doi:10.5455/jjcit.71-1624859356.
Regular Issue Subscription Original Research
Volume 13
Issue 01
Received 20/06/2025
Accepted 25/07/2025
Published 26/02/2026
Publication Time 251 Days
4

Login


My IP

PlumX Metrics