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 sparse representation method based on kernel and virtual samples for face recognition

Optik ◽  
2013 ◽  
Vol 124 (23) ◽  
pp. 6236-6241 ◽  
Author(s):  
Ningbo Zhu ◽  
Ting Tang ◽  
Shi Tang ◽  
Deyan Tang ◽  
Fu Yu
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Virtual Samples ◽  
Representation Method

Virtual samples and sparse representation‐based classification algorithm for face recognition

2018 ◽  
Vol 13 (2) ◽  
pp. 172-177 ◽  
Author(s):  
Yali Peng ◽  
Lingjun Li ◽  
Shigang Liu ◽  
Jun Li ◽  
Han Cao
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Classification Algorithm ◽  
Virtual Samples

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.

Robust classification with available virtual samples and grouped sparse representation strategy in face recognition

2018 ◽  
Vol 65 (19) ◽  
pp. 2209-2219
Author(s):  
Lijun Fu ◽  
Deyun Chen ◽  
Kezheng Lin ◽  
Ao Li ◽  
Hailu Yang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Robust Classification ◽  
Virtual Samples
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