Smart Attendance System Using Face Recognition with OpenCV

Year : 2025 | Volume : 12 | Issue : 03 | Page : 30 39
    By

    Divyanshu Bhardwaj,

  • Nikita,

  • Vernika,

  • Aarush Gupta,

  • Tanya Chauhan,

  1. Student, Department of Computer Science & Engineering, Echelon Institute of Technology, Faridabad, Haryana, India
  2. Assistant Professor, Department of Computer Science & Engineering, Echelon Institute of Technology, Faridabad, Haryana, India
  3. Student, Department of Computer Science & Engineering, Echelon Institute of Technology, Faridabad, Haryana, India
  4. Student, Department of Computer Science & Engineering, Echelon Institute of Technology, Faridabad, Haryana, India
  5. Student, Department of Computer Science & Engineering, Echelon Institute of Technology, Faridabad, Haryana, India

Abstract

In the past, the conventional method of recording student attendance relied heavily on teachers manually marking entries in a physical register. While simple, this approach is not only time-consuming but also highly vulnerable to errors such as accidental omissions, incorrect entries, or even malpractice in the form of proxy attendance. Moreover, traditional registers lack real-time accessibility, making it difficult to analyze or monitor data instantly. To address these limitations, modern systems have introduced biometric solutions such as fingerprint scanning, iris recognition, and other unique identifiers. However, these often require physical contact or specialized devices, which may not always be practical in large classrooms. The proposed project introduces a smart attendance system based on facial recognition technology to overcome these challenges. By capturing classroom images and applying advanced face recognition algorithms through OpenCV, the system can automatically detect and identify students. Attendance is then marked instantly and stored securely. To enhance usability, a Google API is integrated with a Flask-based web application, ensuring real-time database management and easy accessibility through a website. This automated framework not only eliminates the possibility of proxy attendance but also provides a fast, reliable, and scalable solution for educational institutions. The system ultimately aims to simplify attendance management while improving accuracy, efficiency, and transparency.

Keywords: Facial recognition, OpenCV, Google API, Flask framework, smart attendance system, biometric authentication, real-time database management

[This article belongs to Journal of Mobile Computing, Communications & Mobile Networks ]

How to cite this article:
Divyanshu Bhardwaj, Nikita, Vernika, Aarush Gupta, Tanya Chauhan. Smart Attendance System Using Face Recognition with OpenCV. Journal of Mobile Computing, Communications & Mobile Networks. 2025; 12(03):30-39.
How to cite this URL:
Divyanshu Bhardwaj, Nikita, Vernika, Aarush Gupta, Tanya Chauhan. Smart Attendance System Using Face Recognition with OpenCV. Journal of Mobile Computing, Communications & Mobile Networks. 2025; 12(03):30-39. Available from: https://journals.stmjournals.com/jomccmn/article=2025/view=228341


References

  1. Fuzail M, Nouman HM, Mushtaq MO, Raza B, Tayyab A, Talib MW. Face detection system for attendance of class students. International Journal of Multidisciplinary Sciences and Engineering (IJMSE). 2014 Apr; 5(4): 6–10.
  2. Hapani S, Prabhu N, Parakhiya N, Paghdal M. Automated attendance system using image processing. In 2018 IEEE fourth international conference on computing communication control and automation (ICCUBEA). 2018 Aug 16; 1–5.
  3. Akbar MS, Sarker P, Mansoor AT, Al Ashray AM, Uddin J. Face recognition and RFID verified attendance system. In 2018 IEEE International Conference on Computing, Electronics & Communications Engineering (iCCECE). 2018 Aug 16; 168–172.
  4. Okokpujie KO, Noma-Osaghae E, Okesola OJ, John SN, Robert O. Design and implementation of a student attendance system using iris biometric recognition. In 2017 IEEE International Conference on Computational Science and Computational Intelligence (CSCI). 2017 Dec 14; 563–567.
  5. Rathod H, Ware Y, Sane S, Raulo S, Pakhare V, Rizvi IA. Automated attendance system using machine learning approach. In 2017 IEEE International Conference on Nascent Technologies in Engineering (ICNTE). 2017 Jan 27; 1–5.
  6. Siswanto AR, Nugroho AS, Galinium M. Implementation of face recognition algorithm for biometrics based time attendance system. In 2014 IEEE international conference on ICT for smart society (ICISS). 2014 Sep 24; 149–154.
  7. Lukas S, Mitra AR, Desanti RI, Krisnadi D. Student attendance system in classroom using face recognition technique. In 2016 IEEE International Conference on Information and Communication Technology Convergence (ICTC). 2016 Oct 19; 1032–1035.
  8. Emami S, Suciu VP. Facial recognition using OpenCV. Journal of Mobile, Embedded and Distributed Systems (JMEDS). 2012 Mar 30; 4(1): 38–43.
  9. Dev S, Patnaik T. Student attendance system using face recognition. In 2020 IEEE international conference on smart electronics and communication (ICOSEC). 2020 Sep 10; 90–96.
  10. Budiman A, Yaputera RA, Achmad S, Kurniawan A. Student attendance with face recognition (LBPH or CNN): Systematic literature review. Procedia Comput Sci. 2023 Jan 1; 216: 31–8.
  11. Jagneet AG, Budhewar AS, Kadam GR, Jadhav NU, Loharkar KB, Mohabe YR. Face Recognition-Based Attendance System Using Group Photos. Int J Adv Sci Res Eng Trends. 2024 Sep; 8(9): 29–31.
  12. Bhatti KL, Mughal L, Khuhawar FY, Memon SA. Smart Attendance Management System Using Face Recognition. EAI Endorsed Trans Creative Technol. 2018 Oct 1; 5(17): e4.
  13. Alhanaee K, Alhammadi M, Almenhali N, Shatnawi M. Face recognition smart attendance system using deep transfer learning. Procedia Comput Sci. 2021 Jan 1; 192: 4093–102.
  14. Al-Muhaidhri G, Hussain J. Smart attendance system using face recognition. Int J Eng Res Technol. 2019; 8(12): 51–54.
  15. Soundarya S, Ashwini P, Patil SB. A review paper on attendance management system using face recognition. Int J Creat Res Thoughts. 2021; 9(11): 63–8.
  16. Mekala V, Vinod VM, Manimegalai M, Nandhini K. Face recognition based attendance system. Int J Innov Technol Explor Eng. 2019 Oct; 8(12): 520–5.
  17. Khalkar O, Bhosale T, Yadav S, Galande T, Kadam A. Automatic Attendance System, Using Face Detection and Machine Learning. International Journal of Novel Research and Development (IJNRD). 2024; 9(2): 273–277. Available from: https://www.ijnrd.org/papers/IJNRD2402032.pdf.
  18. Singh A, Kalra A, Teotia R, Mamgain S. Smart Campus: Smart Attendance Management System using Face Recognition. Int J Multidiscip Res. 2024; 6(2): 1–11.
  19. Jain A, Gupta R, Shariq M, Ikram T (Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology). Attendance Monitoring System Using Face Recognition. Int J Res Appl Sci Eng Technol. 2022 May; 10(V): 3024–3029.
  20. Raj AA, Shoheb M, Arvind K, Chethan KS. Face recognition based smart attendance system. In 2020 IEEE International conference on intelligent engineering and management (ICIEM). 2020 Jun 17; 354–357.
  21. Patel S, Kumar P, Garg S, Kumar R. Face Recognition based smart attendance system using IOT. Int J Comput Sci Eng. 2018 May; 6(5): 871–7.
  22. Pavithra S, Afshin SH. Face recognition based attendance management system. Int J Eng Res Technol. 2020 May; 09(05): 1190–1192.
  23. Preethi K, Vodithala S. Automated smart attendance system using face recognition. In 2021 IEEE 5th International Conference on Intelligent Computing and Control Systems (ICICCS). 2021 May 6; 1552–1555.
Regular Issue Subscription Original Research
Volume 12
Issue 03
Received 20/06/2025
Accepted 16/09/2025
Published 29/09/2025
Publication Time 101 Days
80


My IP

PlumX Metrics