Design And Implementation Of Face Recognition System Based On Depth Learning

With the continuous improvement of computer vision technology,face recognition has also been rapid development.Face recognition based on deep learning surpasses traditional face recognition and gradually becomes the mainstream technology in the field due to its high recognition rate and recognition speed.Academic,industry and even individual enthusiasts have invested a lot of research efforts.The momentum of development is excellent: it not only continues to refresh records on a large number of public test sets,but also has been successfully applied in areas such as smart security,financial security and public applications.There is no doubt that with the further development of deep learning,face recognition technology in the future will have greater development prospects and market space.This paper mainly studies how to make better use of deep learning tools and apply face recognition technology in engineering practice,especially in Asian face-based applications.The main research contents are as follows:First of all,the function of each module of the system planning and design of the overall framework to depth learning Caffe open-source framework for the platform,C ++ language dynamic link library as a tool to design and implement a video stream capture module,face detection module,face Pre-processing module and face recognition module.Finally completed the face recognition model of training,the overall system.Based on the trained face system model,the face data of Asian countries is organized to migrate and learn.Finally,a face recognition model suitable for practical projects is obtained.The experimental results show that the face recognition system based on deep learning designed and implemented in this paper can achieve the expected results.