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| Fuzzy Decision-making Modular Two-dimensional Principal Component Regression for Robust Face Recognition |
| Zhenyue Zhang, Mingyan Jiang, Xianye Ben, Fei Li |
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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-specific galleries. Lastly, three minimum residuals of every
block and fuzzy similarity preferred ratio decision method are
applied to make a classification. 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.
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| Cite this article: |
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Zhenyue Zhang,Mingyan Jiang,Xianye Ben, et al. Fuzzy Decision-making Modular Two-dimensional Principal Component Regression for Robust Face Recognition[J]. Journal of Fiber Bioengineering and Informatics, 2015, 8(2): 365-372.
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2015, Vol. 8 
