Face Recognition-Based Entry Authentication System for College Cultural Gathering

Abstract

In modern educational institutions, managing and authenticating entry during large-scale events like cultural gatherings can be a challenging task, especially in ensuring secure, quick, and contactless identification of attendees. This project, \"Face Recognition Based Entry Authentication System for College Cultural Gathering\", proposes a smart, automated system leveraging facial recognition technology to address these challenges efficiently. The system utilizes computer vision and machine learning algorithms to identify and authenticate individuals based on their facial features. It captures the face of a student or participant through a camera at the entry point and matches it in real-time with the pre-registered database. If a match is found, access is granted; otherwise, entry is denied. This method eliminates the need for manual identity checks, thereby reducing entry time, preventing unauthorized access, and enhancing overall event security. Developed using Python, OpenCV, and face recognition libraries, and backed by a user-friendly interface, the system ensures ease of use for organizers. Additionally, the system logs entry data, enabling effective monitoring and post-event analysis. With scalability and future integration in mind, the system represents a step toward smarter, tech-driven management of institutional events.

Introduction

The project proposes an AI-driven Face Recognition Based Entry Authentication System to efficiently and securely manage attendee access at college cultural events. Traditional manual checks are slow, error-prone, and susceptible to misuse, whereas this system automates entry using facial recognition technology, eliminating physical IDs or passes.

Key Features:

• Uses computer vision and machine learning (via Python libraries like OpenCV, face_recognition, and Dlib) to detect and identify registered participants in real time through a webcam.

• System architecture includes a user interface (Tkinter/PyQt), AI-based recognition logic, and a database (SQLite/CSV) for storing facial data and logs.

• Accurately matches live faces against stored encodings and records all access attempts with timestamps.

• Provides a contactless, fast, and secure entry experience suitable for managing large crowds.

Current Progress:

• Functional prototype developed and tested under controlled conditions.

• Live face capture and recognition integrated successfully.

• Database management and logging implemented.

• Limitations include reduced accuracy in poor lighting, unusual facial poses, and occlusions like masks.

• Basic security measures in place; advanced features planned.

Challenges and Future Enhancements:

• Improve performance under varied lighting and facial angles.

• Enhance real-time processing capabilities for scalability.

• Add liveness detection to prevent spoofing via photos or videos.

• Automate attendance reporting and analytics for event organizers.

Applications:

• Ensures only authorized individuals enter events.

• Replaces physical passes with contactless authentication.

• Improves security and crowd monitoring.

• Automates attendance tracking and generates insightful reports.

Collaboration and Open-Source Approach:

• Built using open-source tools to foster transparency and extendibility.

• Shared on platforms like GitHub for community collaboration.

• Engages college tech clubs for workshops promoting AI security awareness and innovation.

Conclusion

The development of the Face Recognition Based Entry Authentication System marks a significant step toward automating and securing the process of participant verification at college cultural events. Traditional manual methods such as checking ID cards or name lists are time-consuming, error-prone, and difficult to manage for large crowds. In contrast, the proposed system offers a faster, more efficient, and technologically advanced alternative by leveraging real-time face recognition. Through the integration of computer vision, machine learning, and user interface design, the system successfully identifies registered individuals based on their facial features, enabling seamless access control while maintaining a high level of security. The use of Python, OpenCV, and the face_recognition library allowed for the creation of a lightweight and effective solution capable of recognizing faces with reasonable accuracy under standard conditions. During implementation and testing, the system demonstrated reliable performance, accurately authenticating individuals and preventing unauthorized access. It also helped reduce the workload for event organizers and minimized the risk of human error. However, certain limitations were identified such as performance under low lighting, facial occlusion, and hardware dependency which provide opportunities for future improvement.

References

[1] Face Recognition Using Eigenfaces Vision and Modeling Group” by Turk, Matthew, and Alex Pentland. [2] Biometric Systems: Technology, Design and Performance Evaluation [3] Springer, 2008.” By Khan, Riazuddin [4] Deep Learning MIT Press, 2016.” By Good fellow, Ian, Yoshua Bengio, and Aaron Courville. [5] Introduction to Biometrics Springer, 2011.” By Jain, Anil K., Arun Ross, and Karthik Nandakumar. [6] Pattern Recognition and Machine Learning Springer, 2006.” By Pattern Recognition and Machine Learning Springer, 2006. [7] Digital Image Processing (4th Edition) Pearson, 2018.” By Gonzalez, Rafael C., and Richard E. Woods. [8] Computer Vision: Algorithms and Applications Springer, 2010.” by Szeliski, Richard.

Copyright

Copyright © 2025 Pankaj Pawar, Ratna Nalge, Muktai Panchal, Chaitanya Zunjare. 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.