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Integrating virtual samples and fuzzy discriminant analysis for sparse representation-based face classification

[+] Author Affiliations
Zi Liu, Zhenmin Tang

Nanjing University of Science and Technology, School of Computer Science and Engineering, XiaoLingWei No. 200, Nanjing 210094, China

Xiaoning Song

Jiangnan University, School of Internet of Things Engineering, Li Lake Avenue No. 1800, Wuxi 214122, China

J. Electron. Imaging. 24(2), 023013 (Mar 11, 2015). doi:10.1117/1.JEI.24.2.023013
History: Received October 20, 2014; Accepted February 19, 2015
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Abstract.  Representation-based classification methods, such as sparse representation-based classification, have been a breakthrough for face recognition recently. Under such a philosophy, we develop a framework that fuses virtual synthesized training samples as bases and fuzzy discriminant sparse residuals measurement in face classification. More specifically, the preprocessing of the proposed algorithm aims to alleviate sampling uncertainty by introducing extra training samples and then the features are extracted over the above dictionary using a hierarchical multiscale local binary patterns scheme. The second stage tries to approximate testing samples by a subset of training samples, and the introduced sparse coding process with weak l1-constraint has superior competitiveness in that the accuracy has been improved while the complexity has fallen. The third stage again determines a new weighted sum for the remaining informative samples. Hence, fuzzy sparse similarity grades are designed by the new weighted value, which can be merged into the typical discriminant analysis criterion. Experimental results from various benchmark face databases have demonstrated the effectiveness of our algorithm.

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Citation

Zi Liu ; Xiaoning Song and Zhenmin Tang
"Integrating virtual samples and fuzzy discriminant analysis for sparse representation-based face classification", J. Electron. Imaging. 24(2), 023013 (Mar 11, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.2.023013


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