Design and Implementation of Real-time Face Recognition System Using Deep Learning

To address the challenges of accuracy and speed in real-time face recognition, we propose an end-to-end system based on deep learning. The system adopts a front-end and back-end separation architecture, realizing real-time recognition through modules such as video stream face detection, tracking, feature extraction, and comparison. We improve the FaceNet model and propose FaceNet-Local, enhancing the model’s robustness by introducing local feature representation. The system is comprehensively evaluated on benchmark datasets like LFW and real-world scenarios, achieving leading levels in offline recognition accuracy and online real-time performance. For instance, FaceNet-Local achieves a Rank-1 recognition rate of $\mathbf{9 9. 5 3 \%}$ on LFW, with an end-to-end latency controlled within 150 ms. Future plans involve further algorithm optimization to enhance small-sample learning capability and expand applications in smart communities, security monitoring, etc. We believe this system provides valuable references for the development of real-time face recognition technology.