Online Face Recognition System Based on Convolutional Neural Network (CNN)

Abstract

In the real environment, face recognition based on video or image files is easily affected by the acquisition angle, expression, illumination and other conditions, and general machine learning and pattern recognition face recognition algorithms also have problems of accuracy and adaptability. Therefore, based on the current technical development, this paper takes the deep learning algorithm model as the core, uses MTCNN multi-task convolutional neural network with FaceNet model to complete the face detection and face recognition of video image files, and completes the corresponding data training with the help of Keras framework and TensorFlow class library to form an intelligent machine that can support the call of Web server. At the same time, ASP.NET framework and C# language are used to build the Web server, design and develop each functional module, improve the deployment of the corresponding API interface, build the front-end interactive interface, and form the online face recognition system. The overall design of the system adopts B/S architecture, which supports users to access the Web Server through simple request operation to complete the corresponding face detection and recognition of the input video image files, and return the comparison recognition results to the front page for display. The system will greatly improve the precision and accuracy of face recognition, and make a useful attempt to further expand the application scenarios of face recognition technology.