Face recognition based on singular-value feature vectors

2003 ◽  
Vol 42 (8) ◽  
pp. 2368 ◽  
Author(s):  
Quan Pan ◽  
Min-Gui Zhang ◽  
De-Long Zhou ◽  
Yong-Mei Cheng ◽  
Hong-Cai Zhang
Keyword(s):  
Face Recognition ◽  
Singular Value ◽  
Feature Vectors

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.

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

3D Face Recognition Based on Regional Shape Maps

2018 ◽  
Vol 22 (1) ◽  
pp. 141-146 ◽  
Author(s):  
Xiaoni Wang ◽  
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
3D Face Recognition ◽  
Face Database ◽  
Registration Method ◽  
3D Face ◽  
Feature Vectors ◽  
Input Face ◽  
Point To Point ◽  
3D Face Database

This study proposes an iterative closest shape point (ICSP) registration method based on regional shape maps for 3D face recognition. A neutral expression image randomly selected from a face database is considered as the reference face. The point-to-point correspondences between the input face and the reference face are achieved by constructing the points’ regional shape maps. The distance between corresponding point pairs is then minimized by iterating through the correspondence findings and coordinate transformations. The vectors composed of the closest shape points obtained in the last iteration are regarded as the feature vectors of the input face. These 3D face feature vectors are finally used for both training and recognition using the Fisherface method. Experiments are conducted using the 3D face database maintained by the Chinese Academy of Science Institute of Automation (CASIA). The results show that the proposed method can effectively improve 3D face recognition performance.

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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