Fuzzy Decision-making Modular Two-dimensional Principal Component Regression for Robust Face Recognition
Year: 2015
Journal of Fiber Bioengineering and Informatics, Vol. 8 (2015), Iss. 2 : pp. 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.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.3993/jfbim00121
Journal of Fiber Bioengineering and Informatics, Vol. 8 (2015), Iss. 2 : pp. 365–372
Published online: 2015-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 8
Keywords: Face Recognition