Design and implementation of face recognition system based on deep learning
In order to address the shortcomings of traditional face recognition methods in terms of accuracy and robustness,a novel type of deep learning-based face recognition system is designed and implemented in this study.The system consists of four main modules:face detection,preprocessing,characterization and recognition.The deep convolutional neural network used combines FaceNet model and DRNet structure to optimize feature ex-traction and spatial embedding.The recognition accuracy is improved by using ternary loss function.Experimen-tal results show that the system achieves 99.71%average recognition accuracy on the recognized LFW dataset.In addition,through quantification and pruning techniques,the system achieves low computational complexity and memory requirements while maintaining real-time high accuracy processing.
deep learningface recognitionface detectionFaceNet modelDRNet architecture
国家科技期刊平台
NSTL
万方数据
