Improving the Face Recognition Accuracy under Varying Illumination Conditions for Local Binary Patterns and Local Ternary Patterns Based on Weber-Face and Singular Value Decomposition

2016 ◽  
Author(s):  
Chi-Kien Tran ◽  
Chin-Dar Tseng ◽  
Tsair-Fwu Lee
Keyword(s):  
Face Recognition ◽  
Singular Value Decomposition ◽  
Recognition Accuracy ◽  
Local Binary Patterns ◽  
Singular Value ◽  
Local Ternary Patterns ◽  
The Face ◽  
Value Decomposition

Face Recognition and Semantic Features

2011 ◽  
pp. 80-98
Author(s):  
Huiyu Zhou ◽  
Yuan Yuan ◽  
Chunmei Shi
Keyword(s):  
Face Recognition ◽  
Singular Value Decomposition ◽  
Eigenvalue Problem ◽  
Singular Value ◽  
Semantic Features ◽  
Subspace Analysis ◽  
Geometrical Properties ◽  
The Face ◽  
Tensor Subspace ◽  
Value Decomposition

The authors present a face recognition scheme based on semantic features’ extraction from faces and tensor subspace analysis. These semantic features consist of eyes and mouth, plus the region outlined by three weight centres of the edges of these features. The extracted features are compared over images in tensor subspace domain. Singular value decomposition is used to solve the eigenvalue problem and to project the geometrical properties to the face manifold. They compare the performance of the proposed scheme with that of other established techniques, where the results demonstrate the superiority of the proposed method.

scholarly journals New Fusion of SVD and DCT-LBP for Face Recognition

2016 ◽  
Author(s):  
El Mahdi Barrah ◽  
Said Safi ◽  
Abdessamad Malaoui
Keyword(s):  
Face Recognition ◽  
Singular Value Decomposition ◽  
Local Binary Patterns ◽  
Singular Value ◽  
Experimental Results ◽  
Recognition Algorithms ◽  
Dct Domain ◽  
Value Decomposition

In this paper, we proposed the fusion of two projection based face recognition algorithms: local binary Patterns in DCT domain and singular value decomposition (SVD) characterized by its simplicity and efficiently. Standard databases ORL are used to test the experimental results which prove that proposed system achieves more accurate face recognition as compared to individual method.

Singular value decomposition-based virtual representation for face recognition

2020 ◽  
Vol 31 (3) ◽  
Author(s):  
Shigang Liu ◽  
Yuhong Wang ◽  
Yali Peng ◽  
Sujuan Hou ◽  
Keyou Zhang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition

Singular value decomposition based virtual representation for face recognition

2017 ◽  
Vol 77 (6) ◽  
pp. 7171-7186 ◽  
Author(s):  
Guiying Zhang ◽  
Wenbin Zou ◽  
Xianjie Zhang ◽  
Yong Zhao
Keyword(s):  
Face Recognition ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition
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