Class-specific kernel linear regression classification for face recognition under low-resolution and illumination variation conditions
Yang‐Ting Chou, Shih-Ming Huang, Jar‐Ferr Yang · EURASIP Journal on Advances in Signal Processing · 2016
In this paper, a novel class-specific kernel linear regression classification is proposed for face recognition under very low-resolution and severe illumination variation conditions. Since the low-resolution problem coupled with illumination variations makes ill-posed data distribution, the nonlinear projection rendered by a kernel function would enhance the modeling capability of linear regression for the ill-posed data distribution. The explicit knowledge of the nonlinear mapping function can be avoided by using the kernel trick. To reduce nonlinear redundancy, the low-rank- r approximation is suggested to make the kernel projection be feasible for classification. With the proposed class-specific kernel projection combined with linear regression classification, the class label can be determined by calculating the minimum projection error. Experiments on 8 × 8 and 8 × 6 images down-sampled from extended Yale B, FERET, and AR facial databases revealed that the proposed algorithm outperforms the state-of-the-art methods under severe illumination variation and very low-resolution conditions.