@Article{JFBI-8-2,
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 = {https://global-sci.com/article/86575/fuzzy-decision-making-modular-two-dimensional-principal-component-regression-for-robust-face-recognition}
}