scholarly journals A Fast Iterative Pursuit Algorithm in Robust Face Recognition Based on Sparse Representation

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhao Jian ◽  
Huang Luxi ◽  
Jia Jian ◽  
Xie Yu
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Recognition Performance ◽  
Stopping Rules ◽  
Partial Occlusion ◽  
Slow Response ◽  
Robust Version ◽  
Robust Face Recognition

A relatively fast pursuit algorithm in face recognition is proposed, compared to existing pursuit algorithms. More stopping rules have been put forward to solve the problem of slow response of OMP, which can fully develop the superiority of pursuit algorithm—avoiding to process useless information in the training dictionary. For the test samples that are affected by partial occlusion, corruption, and facial disguise, recognition rates of most algorithms fall rapidly. The robust version of this algorithm can identify these samples automatically and process them accordingly. The recognition rates on ORL database, Yale database, and FERET database are 95.5%, 93.87%, and 92.29%, respectively. The recognition performance under various levels of occlusion and corruption is also experimentally proved to be significantly enhanced.

Learning a Sparse Representation for Robust Face Recognition

2013 ◽  
pp. 233-240
Author(s):  
Weihua Ou ◽  
Xinge You ◽  
Pengyue Zhang ◽  
Xiubao Jiang ◽  
Ziqi Zhu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Robust Face Recognition

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.

scholarly journals Robust face recognition via low-rank sparse representation-based classification

2015 ◽  
Vol 12 (6) ◽  
pp. 579-587 ◽  
Author(s):  
Hai-Shun Du ◽  
Qing-Pu Hu ◽  
Dian-Feng Qiao ◽  
Ioannis Pitas
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Low Rank ◽  
Robust Face Recognition
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