Face matching system in multi-pose changing scene

Due to the emergence of deep learning, face recognition has made remarkable achievements. But in the traditional video surveillance scene, because the monitoring object is not controlled and the face posture changes frequently, the actual face image may be a negative face image, which greatly reduces the accuracy of face recognition. And the traditional monitoring system does not have an automatic warning function, which wastes a lot of human resources. This paper focuses on the realization of a real-time and efficient face matching system in multi-pose changing scenes. The system mainly uses the MTCNN algorithm for face detection and alignment and uses a generative confrontation network to make face positive to eliminate the influence of posture change. The feature extraction network uses lightweight MobileFaceNet as the backbone network for feature extraction, and the feature comparison is carried out by cosine similarity. Experiments show that the multi-pose face matching system can greatly improve the speed of feature extraction, and has good accuracy, real-time, and robustness.