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Journal of Electronic Imaging

Integrating virtual samples and fuzzy discriminant analysis for sparse representation-based face classification
Author(s): Zi Liu; Xiaoning Song; Zhenmin Tang
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Paper 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.

Paper Details

Date Published: 11 March 2015
PDF: 12 pages
J. Electron. Imaging. 24(2) 023013 doi: 10.1117/1.JEI.24.2.023013
Published in: Journal of Electronic Imaging Volume 24, Issue 2
Show Author Affiliations
Zi Liu, Nanjing Univ. of Science and Technology (China)
Xiaoning Song, Jiangnan Univ. (China)
Zhenmin Tang, Nanjing Univ. of Science and Technology (China)


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