A Kernel-based sparse representation method for face recognition

2012 ◽  
Vol 24 (3-4) ◽  
pp. 845-852 ◽  
Author(s):  
Ningbo Zhu ◽  
Shengtao Li
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Representation Method

A novel sparse representation method based on virtual samples for face recognition

2012 ◽  
Vol 24 (3-4) ◽  
pp. 513-519 ◽  
Author(s):  
Deyan Tang ◽  
Ningbo Zhu ◽  
Fu Yu ◽  
Wei Chen ◽  
Ting Tang
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Virtual Samples ◽  
Representation Method

A novel virtual samples-based sparse representation method for face recognition

Optik ◽  
2014 ◽  
Vol 125 (15) ◽  
pp. 3908-3912 ◽  
Author(s):  
Yuyao Wang ◽  
Min Wang ◽  
Yan Chen ◽  
Qi Zhu
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Virtual Samples ◽  
Representation Method

A Kernel Two-Phase Test Sample Sparse Representation for Face Recognition

2015 ◽  
Vol 30 (01) ◽  
pp. 1656001
Author(s):  
Zhonghua Liu ◽  
Jiexin Pu ◽  
Yong Qiu ◽  
Moli Zhang ◽  
Xiaoli Zhang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Recognition Performance ◽  
Mapping Function ◽  
Feature Space ◽  
Test Sample ◽  
Training Data ◽  
Two Phase ◽  
Kernel Parameter ◽  
Representation Method

Sparse representation is a new hot technique in recent years. The two-phase test sample sparse representation method (TPTSSR) achieved an excellent performance in face recognition. In this paper, a kernel two-phase test sample sparse representation method (KTPTSSR) is proposed. Firstly, the input data are mapped into an implicit high-dimensional feature space by a nonlinear mapping function. Secondly, the data are analyzed by means of the TPTSSR method in the feature space. If an appropriate kernel function and the corresponding kernel parameter are selected, a test sample can be accurately represented as the linear combination of the training data with the same label information of the test sample. Therefore, the proposed method could have better recognition performance than TPTSSR. Experiments on the face databases demonstrate the effectiveness of our methods.

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