当前位置:
X-MOL 学术
›
Opt. Mem. Neural Networks
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Comparison of Face Recognition and Detection Models: Using Different Convolution Neural Networks
Optical Memory and Neural Networks Pub Date : 2019-07-01 , DOI: 10.3103/s1060992x19020036 Kai Kang
中文翻译:
人脸识别和检测模型的比较:使用不同的卷积神经网络
更新日期:2019-07-01
Optical Memory and Neural Networks Pub Date : 2019-07-01 , DOI: 10.3103/s1060992x19020036 Kai Kang
Abstract
Face detection and recognition plays an important role in many occasions. This study explored the application of convolutional neural network in face detection and recognition. Firstly, convolutional neural network was briefly analyzed, and then a face detection model including three convolution layers, four pooling layers, introduction layers and three fully connected layers was designed. In face recognition, the self-learning convolutional neural network (CNN) model for global and local extended learning and Spatial Pyramid Pooling (SPP)-NET model were established. LFW data sets were used as model test samples. The results showed that the face detection model had an accuracy rate of 99%. In face recognition, the self-learning CNN model had an accuracy rate of 94.9% accuracy, and the SPP-Net model had an accuracy rate of 92.85%. It suggests that the face detection and recognition model based on convolutional neural network has good accuracy, and the face recognition efficiency of self-learning CNN model was better, which deserves further research and promotion.中文翻译:
人脸识别和检测模型的比较:使用不同的卷积神经网络