Design and implementation of face detection and recognition system based on deep learning

This study proposes and implements a face detection and recognition system based on DL (Deep Learning) to address the challenges and requirements in the field of face detection and recognition. In terms of facial detection, modern DL models are adopted to improve the accuracy and speed of detection. In facial recognition, deep convolutional neural networks are used for feature extraction, and the accuracy of identity recognition is improved by introducing an angle robustness loss function. The experimental results indicate that the system has undergone multiple tests to verify its performance stability and practical application feasibility. The innovation of this study lies in fully utilizing DL technology to improve the performance of facial detection and recognition through training and optimization of large- scale datasets. By exploring different model architectures, loss functions, and training strategies, system performance was optimized and robustness was improved. In summary, this study provides strong support for further research and application in the field of facial detection and recognition, and provides powerful tools and methods for solving security and identity verification problems in the real world.