Improved Principal Component Regression for Face Recognition Under Illumination Variations
Shih‐Ming Huang, Jar‐Ferr Yang · IEEE Signal Processing Letters · 2012
The uncontrollable illumination problem is a great challenge for face recognition. In this paper, we propose a novel face recognition framework, the improved principal component regression classification (IPCRC) algorithm, which could overcome the problem of multicollinearity in linear regression. The IPCRC approach first performs principal component analysis (PCA) process to project the face images onto the face space. The firstnprincipal components are intentionally dropped to boost the robustness against illumination changes. Then, the linear regression classification (LRC) is executed on the projected data and the identity is determined by the minimum reconstruction error. Experiments carried out on Yale B and FERET facial databases reveal that the proposed framework outperforms the state-of-the-art methods and demonstrates promising abilities against severe illumination variation.