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Improved Minimum Squared Error Algorithm with Applications to Face Recognition

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  • Qi Zhu
  • Zhengming Li
  • Jinxing Liu
  • Zizhu Fan
  • Lei Yu
  • Yan Chen

Abstract

Minimum squared error based classification (MSEC) method establishes a unique classification model for all the test samples. However, this classification model may be not optimal for each test sample. This paper proposes an improved MSEC (IMSEC) method, which is tailored for each test sample. The proposed method first roughly identifies the possible classes of the test sample, and then establishes a minimum squared error (MSE) model based on the training samples from these possible classes of the test sample. We apply our method to face recognition. The experimental results on several datasets show that IMSEC outperforms MSEC and the other state-of-the-art methods in terms of accuracy.

Suggested Citation

  • Qi Zhu & Zhengming Li & Jinxing Liu & Zizhu Fan & Lei Yu & Yan Chen, 2013. "Improved Minimum Squared Error Algorithm with Applications to Face Recognition," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-5, August.
    • Handle: RePEc:plo:pone00:0070370
    DOI: 10.1371/journal.pone.0070370
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    1. Meng, Anbo & Wu, Zhenbo & Zhang, Zhan & Xu, Xuancong & Tang, Yanshu & Xie, Zhifeng & Xian, Zikang & Zhang, Haitao & Luo, Jianqiang & Wang, Yu & Yan, Baiping & Yin, Hao, 2024. "Optimal scheduling of integrated energy system using decoupled distributed CSO with opposition-based learning and neighborhood re-dispatch strategy," Renewable Energy, Elsevier, vol. 224(C).
    2. Danfeng Hong & Jian Su & Qinggen Hong & Zhenkuan Pan & Guodong Wang, 2014. "Blurred Palmprint Recognition Based on Stable-Feature Extraction Using a Vese–Osher Decomposition Model," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-13, July.

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