Real-Time Facial Recognition Using YOLOv5Face, ArcFace, and ByteTrack for Efficient Identification

Facial recognition plays a crucial role in security, surveillance, and access control systems. However, existing methods often struggle with accuracy, real-time performance, and efficient tracking in dynamic environments. Methods Used: This paper presents a real-time facial recognition system integrating YOLOv5- Face for robust face detection, ArcFace for high- accuracy feature extraction, and ByteTrack for effective multi-face tracking. The combination of these models ensures precise detection, distinct feature embedding, and efficient tracking under challenging conditions such as occlusions and varying illumination. Results Achieved: Experiments on benchmark datasets demonstrate superior recognition accuracy and computational efficiency compared to traditional methods. The system achieves high precision and recall while maintaining real-time performance. Concluding Remarks: The findings emphasize the model’s effectiveness in real-world applications, including security and surveillance. Future improvements will focus on scalability, privacy considerations, and adaptive tracking under dynamic conditions.