Attendance System Based on Facial Recognition

Year : 2025 | Volume : 03 | Issue : 01 | Page : 28 34
    By

    Gaurav Mishra,

  • Abhishek Maurya,

  • Avinash Dwivedi,

  • Himanshu Sharma,

  • Sameer Awasthi,

  1. Student, Department of Computer Science & Engineering-AI, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
  2. Student, Department of Computer Science & Engineering-AI, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
  3. Student, Department of Computer Science & Engineering-AI, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
  4. Student, Department of Computer Science & Engineering-AI, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
  5. HOD, Department of Computer Science & Engineering-AI & AIML, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India

Abstract

Attendance management is a fundamental aspect of educational institutions and workplaces, ensuring accountability, discipline, and operational efficiency. Traditional methods, such as manual roll calls, RFID cards, and fingerprint scanners, are often time-consuming, error-prone, and susceptible to fraud. This research presents an automated attendance management system utilizing face recognition technology to address these challenges effectively. The proposed system employs OpenCV for real-time image processing, the face recognition library for accurate facial detection and recognition, and Tkinter for an intuitive graphical user interface (GUI). The attendance data is automatically stored in an Excel sheet, allowing easy access, management, and future integration with centralized databases like MySQL. The system ensures accurate identification by comparing captured facial encodings with pre-stored data and recording attendance in real time. Unlike traditional biometric methods that require physical contact, this system leverages non-intrusive face recognition, improving hygiene and user experience. The study also highlights challenges such as variations in lighting, facial occlusions, and scalability issues. To mitigate these, the research explores the potential of deep learning techniques, such as Convolutional Neural Networks (CNNs), for improved recognition accuracy. Future enhancements include multi-face recognition, integration with cloud-based storage for centralized attendance records, and deep learning advancements to enhance detection accuracy under different conditions. This system represents a significant step towards an AI-driven, fully automated attendance tracking mechanism, offering an efficient, reliable, and scalable alternative to conventional methods.

Keywords: Convolutional Neural Networks (CNNs), graphical user interface (GUI), Oriented Gradients (HOG), RFID cards, Python

[This article belongs to International Journal of Electronics Automation ]

How to cite this article:
Gaurav Mishra, Abhishek Maurya, Avinash Dwivedi, Himanshu Sharma, Sameer Awasthi. Attendance System Based on Facial Recognition. International Journal of Electronics Automation. 2025; 03(01):28-34.
How to cite this URL:
Gaurav Mishra, Abhishek Maurya, Avinash Dwivedi, Himanshu Sharma, Sameer Awasthi. Attendance System Based on Facial Recognition. International Journal of Electronics Automation. 2025; 03(01):28-34. Available from: https://journals.stmjournals.com/ijea/article=2025/view=206682


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Regular Issue Subscription Review Article
Volume 03
Issue 01
Received 03/03/2025
Accepted 04/04/2025
Published 08/04/2025
Publication Time 36 Days
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