Visitor Authentication System Based on Face Recognition Technology

Abstract

In the era of rapidly advancing security requirements, traditional visitor authentication methods such as ID cards and passwords often fall short in providing robust protection against unauthorized access. This paper presents a real-time visitor authentication system leveraging face recognition technology powered by the YOLOv8 deep learning model. The proposed solution replaces manual verification and RFID-based systems with an automated, contactless, and intelligent approach that captures live facial data through webcam-enabled devices. YOLOv8 ensures high-speed and accurate face detection, while a deep learning-based recognition module matches the detected faces against a dynamically maintained database of registered users. The architecture is designed to support modular deployment across varied security environments such as corporate offices, smart homes, and public infrastructures. Comprehensive testing validates the system\'s performance, achieving high recognition accuracy and near-instantaneous response times. This work demonstrates the viability of integrating real-time object detection with biometric authentication to enhance security, usability, and scalability in modern access control systems.

Introduction

The text discusses the development and implementation of a real-time, contactless visitor authentication system based on YOLOv8-driven face recognition. With increasing security concerns, traditional authentication methods like passwords and RFID are becoming inadequate due to their vulnerability to forgery and errors. Face recognition is emerging as a robust solution, offering high accuracy, scalability, and automation with minimal user interaction.

Key Contributions:

1. Development of a Real-Time Visitor Authentication System: This system uses YOLOv8 for face detection, which is integrated with facial feature extraction and matching for identity verification. The system is designed for efficient operation on standard hardware and can scale across various environments.

2. Real-Time Operation: The system ensures low latency (1.5-2 seconds per authentication) and supports live video-based authentication, making it suitable for applications in public spaces, smart homes, and enterprises.

3. Advanced Deep Learning Models: YOLOv8, a state-of-the-art object detection framework, is utilized for fast face detection. The system also integrates facial embedding models like FaceNet for accurate identity verification.

System Architecture:

The architecture consists of five modules:

1. Image Acquisition: Captures live video frames using a standard camera.

2. Face Detection: YOLOv8 detects faces in the frames in real-time.

3. Feature Embedding: Facial embeddings are generated using deep learning models.

4. Identity Verification: The extracted embeddings are compared with a database to confirm identity.

5. Access Control and Logging: Grants or denies access based on the verification results, logging all attempts.

Advantages over Traditional Methods:

• Contactless: Unlike RFID or passwords, face recognition is entirely contactless.

• Speed: YOLOv8 ensures real-time performance with minimal latency.

• Security: Face recognition offers higher security than traditional methods, which are prone to theft or fraud.

• Scalability: The system is designed to scale easily for different environments.

Challenges in Real-Time Authentication:

The text also highlights the challenges in deploying face recognition systems, including lighting variability, pose changes, occlusion, and spoofing attacks. The system incorporates strategies like data augmentation, liveness detection, and optimized models to address these issues.

Implementation:

The system was implemented using Python, OpenCV, YOLOv8, and other frameworks like DeepFace/FaceNet for facial feature extraction. It was optimized for edge devices, allowing it to run efficiently on mid-range CPUs with optional GPU support. The system was tested with real-world data, showing high accuracy (~98%) and low false acceptance/rejection rates.

Conclusion

This study presents a real-time visitor authentication system leveraging YOLOv8-based face recognition as a scalable, contactless, and secure alternative to traditional access control mechanisms. By integrating fast and lightweight object detection with deep facial feature embedding and similarity-based identity verification, the system achieves high accuracy and low latency in practical deployment scenarios. The proposed architecture eliminates the limitations of manual verification, RFID cards, and password-based systems by providing automated facial authentication with minimal user intervention. The modular design supports seamless integration with existing surveillance infrastructure, while the Streamlit-based interface enables intuitive user interaction for registration, monitoring, and administration. Experimental results confirm the system\'s effectiveness, with over 98% recognition accuracy and sub-two-second response times. The use of YOLOv8 ensures real-time face detection even under resource-constrained environments, and the use of cosine similarity for facial matching provides robust identity verification. While the system performs reliably under typical lighting and orientation conditions, challenges remain in handling extreme occlusions and spoofing attempts. These limitations open avenues for incorporating advanced liveness detection, multi-factor authentication, and 3D facial modeling in future iterations. Overall, this work demonstrates the viability of integrating deep learning-based facial recognition into real-time visitor authentication workflows, offering a practical solution for enhancing security across homes, offices, and public facilities.

References

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Copyright

Copyright © 2025 Dr. K. SubbaRao, Ravipati Vara Lakshmi, Jennepogu Shifali Wesly, Modugula Sriharsha, Sanagala Sri Teja, Shaik Meer Sharif . 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.