scholarly journals Retraction: Honggang Yu, An efficient face recognition algorithm using the improved convolutional neural network

2019 ◽  
Vol 12 (4-5) ◽  
pp. 901-901
Author(s):  
Editorial Office
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition Algorithm

scholarly journals Face Detection and Recognition Method Based on Improved Convolutional Neural Network

2021 ◽  
Vol 15 ◽  
pp. 774-781
Author(s):  
Zhengqiu Lu ◽  
Chunliang Zhou ◽  
Xuyang Xuyang ◽  
Weipeng Zhang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Rapid Development ◽  
Weighted Average ◽  
Recognition Algorithm ◽  
Equal Treatment ◽  
Learning Technology ◽  
Main Research ◽  
Common Face

with rapid development of deep learning technology, face recognition based on deep convolutional neural network becomes one of the main research methods. In order to solve the problems of information loss and equal treatment of each element in the input feature graph in the traditional pooling method of convolutional neural network, a face recognition algorithm based on convolutional neural network is proposed in this paper. First, MTCNN algorithm is used to detect the faces and do gray processing, and then a local weighted average pooling method based on local concern strategy is designed and a convolutional neural network based on VGG16 to recognize faces is constructed which is finally compared with common convolutional neural network. The experimental results show that this method has good face recognition accuracy in common face databases.

scholarly journals Robust face recognition based on multi-task convolutional neural network

2021 ◽  
Vol 18 (5) ◽  
pp. 6638-6651
Author(s):  
Huilin Ge ◽  
◽  
Yuewei Dai ◽  
Zhiyu Zhu ◽  
Biao Wang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Recognition Algorithm ◽  
Face Images ◽  
Robust Face Recognition

<abstract> <sec><title>Purpose</title><p>Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN).</p> </sec> <sec><title>Methods</title><p>In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN.</p> </sec> <sec><title>Results</title><p>The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect.</p> </sec> <sec><title>Conclusions</title><p>This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency.</p> </sec> </abstract>

scholarly journals Face Recognition Algorithm Based On Convolutional Neural Network

2020 ◽  
Vol 1550 ◽  
pp. 022023
Author(s):  
Shiyuan Xu ◽  
Shicheng Xu ◽  
Zhaojun Wang ◽  
Chengyan Pu ◽  
Qingchuan Meng
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition Algorithm

A Face Recognition Algorithm Based on Angular Distance Loss Function and Convolutional Neural Network

2018 ◽  
Vol 55 (12) ◽  
pp. 121505
Author(s):  
龙鑫 Long Xin ◽  
苏寒松 Su Hansong ◽  
刘高华 Liu Gaohua ◽  
陈震宇 Chen Zhenyu
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Loss Function ◽  
Recognition Algorithm ◽  
Angular Distance
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