scholarly journals Optimized Coefficient Vector and Sparse Representation-Based Classification Method for Face Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 8668-8674 ◽  
Author(s):  
Shigang Liu ◽  
Lingjun Li ◽  
Ming Jin ◽  
Sujuan Hou ◽  
Yali Peng
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Classification Method ◽  
Coefficient Vector

Extended sparse representation-based classification method for face recognition

2018 ◽  
Vol 29 (6) ◽  
pp. 991-1007 ◽  
Author(s):  
Yali Peng ◽  
Lingjun Li ◽  
Shigang Liu ◽  
Jun Li ◽  
Xili Wang
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Classification Method

scholarly journals A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2173 ◽  
Author(s):  
Aixiang He ◽  
Guangfen Wei ◽  
Jun Yu ◽  
Meihua Li ◽  
Zhongzhou Li ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Gas Sensors ◽  
Operating Temperature ◽  
Reconstruction Error ◽  
Classification Method ◽  
Training Samples ◽  
Gas Identification ◽  
Coefficient Vector ◽  
Sparse Representation Classification ◽  
Electronic Nose System

A novel sparse representation classification method (SRC), namly SRC based on Method of Optimal Directions (SRC_MOD), is proposed for electronic nose system in this paper. By finding both a synthesis dictionary and a corresponding coefficient vector, the i-th class training samples are approximated as a linear combination of a few of the dictionary atoms. The optimal solutions of the synthesis dictionary and coefficient vector are found by MOD. Finally, testing samples are identified by evaluating which class causes the least reconstruction error. The proposed algorithm is evaluated on the analysis of hydrogen, methane, carbon monoxide, and benzene at self-adapted modulated operating temperature. Experimental results show that the proposed method is quite efficient and computationally inexpensive to obtain excellent identification for the target gases.

Double layers sparse representation for occluded face recognition

Optik ◽  
2015 ◽  
Vol 126 (21) ◽  
pp. 3016-3019 ◽  
Author(s):  
Shuhuan Zhao ◽  
Zheng-ping Hu
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Double Layers

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
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