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A distance weighted linear regression classifier based on optimized distance calculating approach for face recognition

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Abstract

Linear regression technique is an efficient method to solve face recognition problem. It’s based on the theory that images in the same class will also belong to same linear subspace and they can be represented through a linear equation. However, this method suffers from some misclassification problems for the infinite ductility of regression equation, moreover, it also doesn’t make a proper and full use of the information in each sample. For overcoming these problems, a novel algorithm named the Distance Weighted Regression Classifier (DWLRC) is proposed here. It can be used for face recognition under different expression and illumination conditions through a distance weighted method, and it can also be used for optimizing the error in the final distance calculating stage. Experiments on three benchmarks show the better performance of our DWLRC compared with the traditional LRC and some state-of-art methods.

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Acknowledgements

This work was supported by Shenzhen Science and Technology Plan under grant number JCYJ20180306171938767 and the Shenzhen Foundational Research Funding JCYJ20180507183527919.

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Authors and Affiliations

  1. Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Linlin Tang, Huifen Lu, Zhen Pang & Zhangyan Li

  2. Texas Tech University, Lubbock, TX, USA

    Jingyong Su

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  1. Linlin Tang
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  2. Huifen Lu
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  3. Zhen Pang
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  4. Zhangyan Li
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  5. Jingyong Su
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Correspondence to Linlin Tang.

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Tang, L., Lu, H., Pang, Z. et al. A distance weighted linear regression classifier based on optimized distance calculating approach for face recognition. Multimed Tools Appl 78, 32485–32501 (2019). https://doi.org/10.1007/s11042-019-07943-0

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  • DOI: https://doi.org/10.1007/s11042-019-07943-0

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