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Mathematical Problems in Engineering
Volume 2015, Article ID 641510, 13 pages
Research Article

A Fuzzy Kernel Maximum Margin Criterion for Image Feature Extraction

1College of Mathematics and Computer Science, Guangxi University for Nationalities, Nanning 530006, China
2Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning 530006, China
3The China-ASEAN Study Center of Guangxi University for Nationalities, Nanning 530006, China

Received 12 November 2014; Revised 24 March 2015; Accepted 24 March 2015

Academic Editor: Hari M. Srivastava

Copyright © 2015 Shibin Xuan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Based on kernel principal component analysis, fuzzy set theory, and maximum margin criterion, a novel image feature extraction and recognition method, called fuzzy kernel maximum margin criterion (FKMMC), is proposed. In the proposed method, two new fuzzy scatter matrixes are redefined. The new fuzzy scatter matrix can reflect fully the relation between fuzzy membership degree and the offset of the training sample to subclass center. Besides, a concise reliable computational method of the fuzzy between-class scatter matrix is provided. Experimental results on four face databases (AR, extended Yale B, GTFD, and FERET) demonstrate that the proposed method outperforms other methods.