Real-Time Student Attendance System Using AI and Face Recognition

Abstract

The demand for efficient and accurate attendance systems in educational institutions has grown substantially, motivating the development of a contact-free, AI-driven solution. This research presents a Real-Time Student Attendance System that leverages computer vision and facial recognition to automate attendance recording with high accuracy. The system captures student faces using a standard webcam, applies Haar cascade detection and Local Binary Pattern Histogram (LBPH) recognition, and logs attendance automatically into structured CSV files. A user-friendly Tkinter GUI facilitates module navigation—student registration, model training, real-time recognition, and report generation—while supporting manual override when necessary. The system’s modular architecture ensures seamless integration of components and robust performance under varying environmental conditions. Testing demonstrates over 95% recognition accuracy, with immediate generation of attendance summaries and real-time GUI feedback. The proposed system reduces administrative load, prevents proxy marking, and offers scalability as a practical, low-cost solution for modern classrooms.

Introduction

Purpose & Problem

Traditional attendance methods (manual roll-call, RFID, fingerprint) are:

• Time-consuming

• Error-prone

• Vulnerable to proxy attendance

• Unhygienic (especially fingerprint-based)

???? Project Objective

To develop a contactless, accurate, and automated attendance system using Face Recognition and AI, capable of:

• Real-time face detection

• Training and recognizing students

• Auto-generating attendance records

• Operating under various classroom conditions

???? Methodology

The system is implemented using:

• Image Capture: Real-time webcam capture of student faces

• Preprocessing: Convert to grayscale, normalize lighting

• Model Training: Using LBPH (Local Binary Pattern Histogram) via OpenCV

• Recognition & Attendance: Match live faces to trained data and record in CSV

• GUI: Built with Tkinter for admin/faculty to register students, train models, mark attendance, and view reports

???? Literature & Existing Systems

• Traditional systems (RFID, fingerprint) face security, cost, and hygiene issues

• Recent AI-powered systems using deep learning (CNN, Haar, LBPH) offer better accuracy

• This project builds on these by improving performance under lighting/pose variation using preprocessing and robust training

???? Proposed System Features

• Non-intrusive & contactless attendance

• Tkinter GUI for ease of use

• OpenCV-based recognition (LBPH & Haar Cascade)

• Real-time attendance recorded and stored in CSV

• Cost-effective (only needs Python & webcam)

?? Feasibility Analysis

???? Tools & Technologies

• Python: Main programming language

• OpenCV: Face detection (Haar Cascade), recognition (LBPH)

• Tkinter: GUI development

• CSV: Attendance and student data storage

• Libraries: NumPy, Pandas, PIL

• Hardware: Webcam and standard PC/laptop

???? System Design Components

1. System Architecture: Modular — Image Capture → Model Training → Recognition → Attendance Recording

2. Context Diagram: Shows interaction between students, faculty/admin, system, and database

3. Use Case Diagram: Admin registers and manages attendance; students are passively recognized

4. Data Flow Diagram (DFD): Logical flow of data between capture, recognition, and storage

5. Class Diagram: Object-oriented view of core classes — Student, Admin, Recognizer, Attendance, Database

6. Sequence Diagram: Step-by-step communication between system actors during registration, training, recognition, and attendance reporting

???? Outcome

The system achieves:

• Accurate, real-time face-based attendance

• Elimination of proxy/human errors

• Seamless integration with classroom routines

• Scalable and adaptable framework for institutions

Conclusion

The he Real-Time Student Attendance System using Face Recognition and AI presents an innovative approach to solving the limitations of conventional attendance systems. Traditional methods such as manual roll calls or RFID cards are prone to inaccuracies, proxy attendance, and require significant administrative effort. By leveraging artificial intelligence, computer vision, and machine learning algorithms, this project introduces a seamless, contactless, and automated solution for maintaining accurate attendance records. The use of the LBPH (Local Binary Patterns Histogram) algorithm ensures that the system performs reliably in diverse lighting and background conditions, while the integration of a webcam and Tkinter-based GUI makes the solution user-friendly and accessible. Experimental testing confirmed that the system performs effectively in real-world classroom environments, ensuring quick recognition, error-free attendance logging, and secure data storage in CSV files. The modular design, consisting of image capture, model training, recognition, and attendance recording, enhances system flexibility and scalability. Moreover, the incorporation of real-time face recognition provides transparency and eliminates unfair practices such as proxy attendance. Faculty members can easily generate and monitor reports, making the system a practical tool for academic institutions. In conclusion, this project not only demonstrates the potential of AI-based automation in the education sector but also establishes a foundation for future advancements. The system is low-cost, efficient, and scalable, making it suitable for schools, colleges, and universities. It provides a significant step towards digital transformation in classroom management and paves.

References

[1] 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. [2] T. Ahonen, A. Hadid, and M. Pietikäinen, \"Face Recognition with Local Binary Patterns,\" IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037–2041, Dec. 2006. [3] F. Schroff, D. Kalenichenko, and J. Philbin, \"FaceNet: A Unified Embedding for Face Recognition and Clustering,\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815–823, 2015. [4] M. Turk and A. Pentland, \"Eigenfaces for Recognition,\" Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 1991. [5] R. Ranjan, S. Sankaranarayanan, C. D. Castillo, and R. Chellappa, \"An All-in-One Convolutional Neural Network for Face Analysis,\" IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 17–24, 2017. [6] S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, \"Face Recognition: A Convolutional Neural-Network Approach,\" IEEE Transactions on Neural Networks, vol. 8, no. 1, pp. 98–113, Jan. 1997. [7] A. Krizhevsky, I. Sutskever, and G. E. Hinton, \"ImageNet Classification with Deep Convolutional Neural Networks,\" Advances in Neural Information Processing Systems (NIPS), pp. 1097–1105, 2012. [8] International Journal for Research in Applied Science and Engineering Technology (IJRASET), \"Paper Submission Guidelines,\" [Online]. Available: https://www.ijraset.com. [9] M. H. Yap, R. Hachiuma, A. Alavi, et al., \"Deep Learning in Face and Object Recognition: A Comprehensive Evaluation,\" Computers in Biology and Medicine, vol. 135, p. 104596, 2021. [10] A. Geitgey, \"Face Recognition with Python,\" Medium Technical Blog, 2018. [Online]. Available: https://medium.com/@ageitgey

Copyright

Copyright © 2025 Vaishnavi Lambu, Shruthi Rampure. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.