Kernel linear regression for low resolution face recognition under variable illumination
Shih‐Ming Huang, Jar‐Ferr Yang · 2012
To improve the limitation of linear regression classification, a class specific kernel linear regression classification is proposed for low resolution face recognition under variable illumination. The nonlinear mapping function enhances the modeling capability for highly nonlinear data distribution. The explicit knowledge of the nonlinear mapping function can be avoided computationally by using the kernel trick. With kernel projection, the class label is also determined by calculating the minimum reconstruction error. Experiments carried out on Yale B facial database in size of 8×8 pixels reveal that the proposed algorithm outperforms the state-of-the-art methods and demonstrates promising abilities against severe illumination variation.