Face Recognition Algorithm Based on Sparse Representation of DAE Convolution Neural Network

2018 ◽  
Vol 10 (4) ◽  
pp. 290-298 ◽  
Author(s):  
Yuancheng Li ◽  
Yan Li
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Sparse Representation ◽  
Recognition Algorithm ◽  
Convolution Neural Network

scholarly journals Research on Face Recognition Algorithm Based on Improved Residual Neural Network

2021 ◽  
Vol 9 (1) ◽  
pp. 46
Author(s):  
Tang Xiaolin ◽  
Wang Xiaogang ◽  
Hou Jin ◽  
Han Yiting ◽  
Huang Ye
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Recognition Algorithm

scholarly journals Research on software credibility algorithm based on deep convolutional sparse coding

2021 ◽  
Vol 336 ◽  
pp. 08013
Author(s):  
Zhaosheng Xu
Keyword(s):  
Neural Network ◽  
Sparse Representation ◽  
Sparse Coding ◽  
Classification System ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network

Based on the author's research time, this paper studies the software credibility algorithm based on deep convolutional sparse coding. Firstly, it summarizes the convolutional sparse coding and trust classification system, and then constructs the algorithm from two aspects: factor processing based on deep convolution neural network and trust classification based on sparse representation.

MCE-BASED FACE RECOGNITION

2001 ◽  
Vol 15 (08) ◽  
pp. 1311-1327 ◽  
Author(s):  
LIANG-HUA CHEN ◽  
SHAO-HUA DENG ◽  
HONG-YUAN LIAO
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Face Detection ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Classification Error ◽  
Complex Scenes ◽  
Minimum Classification Error ◽  
Complete Procedure ◽  
Human Faces

This paper proposes a complete procedure for the extraction and recognition of human faces in complex scenes. The morphology-based face detection algorithm can locate multiple faces oriented in any direction. The recognition algorithm is based on the minimum classification error (MCE) criterion. In our work, the minimum classification error formulation is incorporated into a multilayer perceptron neural network. Experimental results show that our system is robust to noisy images and complex background.

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