A Survey on Face Recognition Using Convolutional Neural Network

2020 ◽  
pp. 649-661 ◽  
Author(s):  
M. Swapna ◽  
Yogesh Kumar Sharma ◽  
B. M. G. Prasad
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network

Two-stream deep convolutional neural network approach for RGB-D face recognition

2021 ◽  
Author(s):  
P. Shunmugam ◽  
K. Kamarudin ◽  
L. M. Kamarudin ◽  
A. Zakaria ◽  
H. Nishizaki
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Network Approach ◽  
Neural Network Approach

Genetic algorithm‐optimised structure of convolutional neural network for face recognition applications

2016 ◽  
Vol 10 (6) ◽  
pp. 559-566 ◽  
Author(s):  
Arash Rikhtegar ◽  
Mohammad Pooyan ◽  
Mohammad Taghi Manzuri‐Shalmani
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Face Recognition ◽  
Convolutional Neural Network

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

A Convolutional Neural Network Based on Feature Fusion for Face Recognition

2020 ◽  
Vol 57 (10) ◽  
pp. 101508 ◽  
Author(s):  
王嘉欣 Wang Jiaxin ◽  
雷志春 Lei Zhichun
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Feature Fusion
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