Research And Implementation Of Multi-view Face Recognition

With the increasing degree of informatization in today's society,the realization of face recognition technology has received widespread attention by extracting facial features.At present,face recognition has been widely used in the fields of public security,multimedia and identity authentication.Although face recognition has a high recognition rate under controlled conditions,the current face recognition methods are still incapable of facing uncontrollable factors in real life application scenarios such as viewing angle,posture,lighting and the like.When meeting these application requirements,the recognition rate will drop sharply.To apply face recognition to the actual scene,changes in the pose of face are one of the most common uncontrollable factors.This paper achieves the face prediction model based on the XGBoost regression,which can automatically realize the multi-pose face correction,so as to obtain the predicted positive face,and this article is performed on the face after the positive turn.The image is repaired so that you can get a composite positive face that is closer to the true face.The main task of this paper is to realize the correction of multi-pose face into a positive face,further repair the face based on the synthesis of face,and get a more close to the real face,and finally achieve multi-pose face recognition system according to the positive face.The main improvements and innovations made in this paper are as follows:(1)A single angle multi-pose face images is blended.The face of a non-cooperated person or a person who is captured in a complex environment is very different from that obtained under a standard environment,which leads to greater difficulties in performing face recognition at a later stage.This paper realizes that a multi-pose face can be converted to a positive face based on one picture.It can obtain the prediction model of facial contour feature points through the training of XGBoost model,and realize the texture coverage of the human face by the segmentation affine change based on the triangle model.Through this method,it is possible to automatically realize multi-pose face normalization,thereby obtaining a picture with an approximately positive face.(2)Sloving the missing pixels problem after generating face.This paper using a method based on approximate FFM parity symmetry is adopted.According to the characteristics of the approximate symmetry of human face,the approximate symmetry of human face is used in the reconstruction of human face.In the face detail part,the approximate FFM is used for correction,thereby solving the problem of missing pixels on the front face due to the multi-pose face.Experiments show that this method can effectively overcome the interference caused by missing pixels,thus effectively improving the recognition rate.(3)System implementation: Using the above methods to implement the current multi-pose face recognition system,the system can effectively recognize the multi-pose face,and replace other multi-pose face normalization methods into the system,and the positive face normalization and other methods are compared and analyzed.For the purpose of realizing the multi-pose face recognition system,this paper has a deep research on the multi-pose face structure of a single angle of view.The experiment proves the feasibility of the method and improves the recognition rate of the system to some extent.The accuracy rate has certain positive significance for the face recognition technology in the actual scene.