Volume 8 Number 3 (Mar. 2013)
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JSW 2013 Vol.8(3): 724-730 ISSN: 1796-217X
doi: 10.4304/jsw.8.3.724-730

Enhancing Kernel Maximum Margin Projection for Face Recognition

Ziqiang Wang, Xia Sun
Henan University of Technology, Zhengzhou, 450001, China

Abstract—To efficiently deal with the face recognition problem, a novel face recognition algorithm based on enhancing kernel maximum margin projection(MMP) is proposed in this paper. The main contributions of this work are as follows. First, the nonlinear extension of MMP through kernel trick is adopted to capture the nonlinear structure of face images. Second, the kernel deformation technique is proposed to increase the discriminating capability of original input kernel function. Third, the feature vector selection approach is applied to improve computational efficiency of kernel MMP. Finally, the multiplicative update rule is employed to enhance training speed of SVM classifier for face recognition. Experimental results on face recognition demonstrate the effectiveness and efficiency of the proposed algorithm.

Index Terms—Face recognition, kernel maximum margin projection, support vector machine(SVM), pattern recognition.

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Cite: Ziqiang Wang, Xia Sun, "Enhancing Kernel Maximum Margin Projection for Face Recognition," Journal of Software vol. 8, no. 3, pp. 724-730, 2013.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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