Identity management based on PCA and SVM

Lixin Shen, Hong Wang (), Li Da Xu, Xue Ma, Sohail Chaudhry and Wu He
Additional contact information
Lixin Shen: Dalian Maritime University
Hong Wang: North Carolina A&T State University
Li Da Xu: Old Dominion University
Xue Ma: Dalian Maritime University
Sohail Chaudhry: Villanova University
Wu He: Old Dominion University

Information Systems Frontiers, 2016, vol. 18, issue 4, No 6, 716 pages

Abstract: Abstract A new approach for face recognition, based on kernel principal component analysis (KPCA) and support vector machines (SVMs), is presented to improve the recognition performance of the method based on principal component analysis (PCA). This method can simultaneously be applied to solve both the over-fitting problem and the small sample problem. The KPCA method is performed on every facial image of the training set to get the core facial features of the training samples. To ensure that the loss of the image information will be as less as possible, the facial data of high-dimensional feature space is projected into low-dimensional space, and then the SVM face recognition model is established to identify the low-dimensional space facial data. Our experimental results demonstrate that the approach proposed in this paper is efficient, and the recognition accuracy of the proposed method reaches 95.4 %.

Keywords: Kernel principal component analysis; Support vector machine; Kernel function methods; Face recognition; Pattern recognition (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10796-015-9551-8

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