Volume 8, Issue 2
Fuzzy Decision-making Modular Two-dimensional Principal Component Regression for Robust Face Recognition

Zhenyue Zhang, Mingyan Jiang, Xianye Ben & Fei Li

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 365-372.

Published online: 2015-08

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  • Abstract

To improve robustness of Linear Regression (LR) for face recognition, a novel face recognition framework based on modular two-dimensional Principal Component Regression (2DPCR) is proposed in this paper. Firstly, all face images are partitioned into several blocks and the approach performs 2DPCA process to project the blocks onto the face spaces. Then, LR is used to obtain the residuals of every block by representing a test image as a linear combination of class-speci c galleries. Lastly, three minimum residuals of every block and fuzzy similarity preferred ratio decision method are applied to make a classi cation. The proposed framework outperforms the state-of-the-art methods and demonstrates strong robustness under various illumination, pose and occlusion conditions on several face databases.

  • Keywords

Face Recognition Block 2DPCR Liner Regression Fuzzy Similarity Preferred Ratio Decision

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@Article{JFBI-8-365, author = {}, title = {Fuzzy Decision-making Modular Two-dimensional Principal Component Regression for Robust Face Recognition}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {2}, pages = {365--372}, abstract = {To improve robustness of Linear Regression (LR) for face recognition, a novel face recognition framework based on modular two-dimensional Principal Component Regression (2DPCR) is proposed in this paper. Firstly, all face images are partitioned into several blocks and the approach performs 2DPCA process to project the blocks onto the face spaces. Then, LR is used to obtain the residuals of every block by representing a test image as a linear combination of class-speci c galleries. Lastly, three minimum residuals of every block and fuzzy similarity preferred ratio decision method are applied to make a classi cation. The proposed framework outperforms the state-of-the-art methods and demonstrates strong robustness under various illumination, pose and occlusion conditions on several face databases.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim00121}, url = {http://global-sci.org/intro/article_detail/jfbi/4717.html} }
TY - JOUR T1 - Fuzzy Decision-making Modular Two-dimensional Principal Component Regression for Robust Face Recognition JO - Journal of Fiber Bioengineering and Informatics VL - 2 SP - 365 EP - 372 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbim00121 UR - https://global-sci.org/intro/article_detail/jfbi/4717.html KW - Face Recognition KW - Block 2DPCR KW - Liner Regression KW - Fuzzy Similarity Preferred Ratio Decision AB - To improve robustness of Linear Regression (LR) for face recognition, a novel face recognition framework based on modular two-dimensional Principal Component Regression (2DPCR) is proposed in this paper. Firstly, all face images are partitioned into several blocks and the approach performs 2DPCA process to project the blocks onto the face spaces. Then, LR is used to obtain the residuals of every block by representing a test image as a linear combination of class-speci c galleries. Lastly, three minimum residuals of every block and fuzzy similarity preferred ratio decision method are applied to make a classi cation. The proposed framework outperforms the state-of-the-art methods and demonstrates strong robustness under various illumination, pose and occlusion conditions on several face databases.
Zhenyue Zhang, Mingyan Jiang, Xianye Ben & Fei Li. (2019). Fuzzy Decision-making Modular Two-dimensional Principal Component Regression for Robust Face Recognition. Journal of Fiber Bioengineering and Informatics. 8 (2). 365-372. doi:10.3993/jfbim00121
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