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Identity management based on PCA and SVM

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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 %.

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Authors and Affiliations

  1. College of Transportation Management, Dalian Maritime University, Dalian, China

    Lixin Shen & Xue Ma

  2. School of Business and Economics, North Carolina A&T State University, Greensboro, NC, 27411, USA

    Hong Wang

  3. School of Information, Yunnan University of Finance and Economics, Kunming, China

    Hong Wang

  4. Old Dominion University, Norfolk, VA, 23529, USA

    Li Da Xu & Wu He

  5. Villanova University, Villanova, PA, 19085, USA

    Sohail Chaudhry

Authors
  1. Lixin Shen
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  2. Hong Wang
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  3. Li Da Xu
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  4. Xue Ma
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  5. Sohail Chaudhry
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  6. Wu He
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Corresponding author

Correspondence to Hong Wang.

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Shen, L., Wang, H., Xu, L.D. et al. Identity management based on PCA and SVM. Inf Syst Front 18, 711–716 (2016). https://doi.org/10.1007/s10796-015-9551-8

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  • DOI: https://doi.org/10.1007/s10796-015-9551-8

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