
To meet demands for efficiency and security, this solution employs deep learning as its core, establishing a ‘front-end capture + back-end processing’ architecture. The front-end utilises high-definition cameras and snapshot devices for real-time facial capture, while the back-end integrates MTCNN detection and ArcFace feature extraction and comparison algorithms. This achieves over 99.8% recognition accuracy with millisecond-level response times.
The solution covers multiple scenarios: access control terminals incorporate liveness detection to prevent fraud, attendance terminals automatically calculate working hours, and surveillance terminals provide real-time alerts for unauthorised individuals. Data transmission and storage employ encryption, complying with the Personal Information Protection Law to safeguard privacy. The system integrates seamlessly with OA and security platforms, reducing costs and enhancing efficiency while balancing security with practicality.

