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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63280

Real-Time Based Facial Recognition Driven Attendance System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63280

Volume:

10 |

5 |

May 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Mrunmayee Rajendra Ghadge

Mrunmayee Rajendra Ghadge

Department of Computer Engineering

SMT. Kashibai Navale College Of Engineering,Pune

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Dr. P. N. Railkar

Department of Computer Engineering

SMT. Kashibai Navale College Of Engineering,Pune

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Abstract

The proposed system presents an automated attendance management framework using Convolutional Neural Networks (CNN) for accurate and efficient face recognition. The system captures live images through a camera, detects faces, and compares them with trained facial data using deep learning algorithms integrated with OpenCV libraries. It automates subject-wise attendance marking and generates detailed monthly attendance reports, minimizing manual intervention and errors. This approach ensures real-time monitoring, transparency, and accuracy in attendance tracking for institutions and organizations. By leveraging CNN for robust feature extraction and recognition, the system performs effectively under varying lighting and pose conditions, offering a secure, scalable, and intelligent solution for modern attendance management.

KEYWORDS

Key Words: Face Recognition Convolutional Neural Network (CNN) Deep Learning Image Processing OpenCV Graphical User Interface (GUI) Automated Attendance System Real-Time Detection Machine Learning Feature Extraction Artificial Intelligence (AI)

Cite This Article

Mrunmayee Rajendra Ghadge, Dr. P. N. Railkar, "Real-Time Based Facial Recognition Driven Attendance System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-4, May 2026.

Full Article/Conclusion

The Automated Attendance System provides an efficient and reliable alternative to the traditional manual attendance process. By integrating a graphical user interface (GUI) and dataset, the system ensures accuracy, reduces human errors, and saves time. This initial phase of development successfully establishes the foundation for seamless attendance management within institutions and organizations. The current implementation demonstrates how automation can improve productivity and maintain data integrity compared to conventional methods.  

References

1)   Heena and M. Banu, “Enhancing Attendance Management: A Novel Approach Using Advanced Face Recognition Technology,” Integrating AI in Science, Management and Technology (AISMT 2025), CCIS, vol. 2699, Springer, Cham, pp. 223–238, Nov. 2025. 2)   G. R. Kadam, N. U. Jadhav, M. A. Wassay, Y. R. Mohabe, K. B. Loharkar, and D. S. Kushwaha, “Facial Recognition-Based Classroom Attendance System with Real-Time Group Photo Processing Using Machine Learning Approach,” Proceedings of the International Conference on Computer Science \& Communication Engineering (ICCSCE 2025), Atlantis Press, pp. 112–120, Nov. 2025. 3)   M. Syarif and W. Gunawan, “Attendance System Leveraging Haar Cascade Detection and CNN-Based FaceNet Recognition Technology,” JOIV: International Journal on Informatics Visualization, vol. 9, no. 2, pp. 708–717, 2025. 4)   K. H. Tran, D. X. Minh-Duc, M. H. Ha, D. T. Pham, and C. D. Truong, “Deployment-Affordable Automatic Student Attendance System Utilizing Face Recognition,” Modelling, Computation and Optimization in Information Systems and Management Sciences (MCO 2025), LNNS vol. 1689, Springer, pp. 187–198, 2025. 5)   T. Abderraouf, A. A. Wassim, and S. Larabi, “An Embedded Intelligent System for Attendance Monitoring,” arXiv preprint arXiv:2406.13694 [cs.CV], June 2024. 6)   T. Nguyen-Tat, M. Q. Bui, and V. M. Ngo, “Automating Attendance Management in Human Resources: A Design Science Approach Using Computer Vision and Facial Recognition,” arXiv preprint arXiv:2405.12633 [cs.CV], May 2024. 7)   P. R. Reddy, S. N. Kumar, and V. K. Sharma, “Face Recognition Based Attendance System Using Eigenfaces, PCA and CNN,” International Journal for Multidisciplinary Research (IJFMR), vol. 6, no. 6, pp. 54–60, Nov.–Dec. 2024. 8)   H. Li, Q. Zhou, and Y. Liu, “Design and Implementation of Face Recognition Attendance System Based on PyQt5,” Proceedings of the International Conference on Artificial Intelligence and Data Technology (ICAIDT 2024), IEEE Computer Society, pp. 320–326, 2024. 9)   Budiman, “Student Attendance with Face Recognition Using LBPH and CNN Models,” Procedia Computer Science, vol. 228, pp. 1250–1257, Elsevier, 2024. 10)            S. Hussain, H. Ullah, S. Zeb, and A. Khan, “An Integrated Computer Vision Pipeline for Face-Based Attendance: Detection, Recognition and Secure Logging,” International Journal of Computer Applications, vol. 187, no. 42, pp. 62–68, Sept. 2025.  

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Publication Date

5/15/2026

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Review Rounds: 2

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