Research And Implementation Of Multi-pose Face Recognition Technology For Video Surveillance

Identification is an important research area in the field of information security.With the rapid development of image processing technology and machine learning technology,face recognition has become one of the most reliable identification technologies and has been widely used in daily life.Applying face recognition technology in the video surveillance system can take full advantage of its early warning capability,which has a positive effect on social stability and prosperity.However,due to the frequent face pose changes and low resolution of video images,the accuracy of face recognition algorithms has severely decreased in video surveillance scenarios.To solve the above problem,this paper proposes a face frontalization algorithm that integrates pose estimation information and a multi-pose face recognition algorithm for video surveillance.The main work of this paper is as follows:(1)To solve problem of frequent face pose changes in video surveillance scenarios,this paper proposes a face frontalization algorithm that incorporates pose information.The algorithm inputs a face image with corresponding face pose information into a trained generation adversarial network,and uses the generator of generation adversarial network to generate a virtual frontal face image,so to achieve the pose frontalization.Experiments show that the face frontalization algorithm that incorporates pose information can not only generate clear and realistic virtual frontal face images,but also better preserve the identity feature of the original face image.(2)To solve the problem that the accuracy of the face recognition algorithm decreases in the video surveillance scenarios,this paper proposes a multi-pose face recognition algorithm for video surveillance.The algorithm makes full use of the characteristics of continuous and smooth face pose changes in video surveillance scenarios,improves the accuracy of multi-pose face recognition through face tracking,face pose estimation and face frontalization.In addition,the algorithm further improves the speed of the multi-pose face recognition by optimizing the face detection algorithm and implements a lightweight face feature extraction algorithm.Experiments show that the multi-pose face recognition algorithm for video surveillance can achieve a face recognition accuracy of 90 % in video surveillance scenarios and has a high robustness for face pose changes,which can meet the application requirements of video surveillance systems.(3)This paper designs and implements a real-time multi-pose face recognition surveillance system based on the multi-pose face recognition algorithm for video surveillance.The system applies a semi-synchronous and semi-asynchronous thread pool as infrastructure,and includes 6 functional modules: multi-channel video stream decoding module,face detection and alignment module,face frontalization module,face recognition module,database module and alarm module.The running results of the system shows that the multi-pose face recognition algorithm proposed in this paper meets the design requirements and has high practical value.