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Identity management based on PCA and SVM

Author

Listed:
  • Lixin Shen

    (Dalian Maritime University)

  • Hong Wang

    (North Carolina A&T State University
    Yunnan University of Finance and Economics)

  • Li Da Xu

    (Old Dominion University)

  • Xue Ma

    (Dalian Maritime University)

  • Sohail Chaudhry

    (Villanova University)

  • Wu He

    (Old Dominion University)

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 %.

Suggested Citation

  • Lixin Shen & Hong Wang & Li Da Xu & Xue Ma & Sohail Chaudhry & Wu He, 2016. "Identity management based on PCA and SVM," Information Systems Frontiers, Springer, vol. 18(4), pages 711-716, August.
    • Handle: RePEc:spr:infosf:v:18:y:2016:i:4:d:10.1007_s10796-015-9551-8
    DOI: 10.1007/s10796-015-9551-8
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    References listed on IDEAS

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    1. Shouhui Pan & Li Wang & Kaiyi Wang & Zhuming Bi & Siqing Shan & Bo Xu, 2014. "A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(3), pages 383-397, May.
    2. Du, Shichang & Lv, Jun, 2013. "Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes," International Journal of Production Economics, Elsevier, vol. 141(1), pages 377-387.
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    Cited by:

    1. Mengyue Wang & Xin Li & Patrick Y. K. Chau, 2021. "Leveraging Image-Processing Techniques for Empirical Research: Feasibility and Reliability in Online Shopping Context," Information Systems Frontiers, Springer, vol. 23(3), pages 607-626, June.
    2. Eric Golinko & Xingquan Zhu, 2019. "Generalized Feature Embedding for Supervised, Unsupervised, and Online Learning Tasks," Information Systems Frontiers, Springer, vol. 21(1), pages 125-142, February.
    3. Borong Zou & Hong Wang & Hui Li & Ling Li & Yuhan Zhao, 2022. "Predicting stock index movement using twin support vector machine as an integral part of enterprise system," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 428-439, May.

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