Kernel linear regression for face recognition
Yuwu LuXiaozhao FangBinglei Xie · 2014
Linear regression uses the least square algo- rithm to solve the solution of linear regression equation. Linear regression classification (LRC) shows good classi- fication performance on face image data. However, when the axes of linear regression of class-specific samples have intersections, LRC could not well classify the samples that distribute around intersections. Moreover, the LRC could not perform well at the situation of severe lighting varia- tions. This paper proposes a new classification method, kernel linear regression classification (KLRC), based on LRC and the kernel trick. KLRC is a nonlinear extension of LRC and can offset the drawback of LRC. KLRC implic- itly maps the data into a high-dimensional kernel space by using the nonlinear mapping determined by a kernel function. Through this mapping, KLRC is able to make the data more linearly separable and can perform well for face recognition with varying lighting. For comparison, we conduct on three standard databases under some evaluation protocols. The proposed methodology not only outper- forms LRC but also takes the better performance than typical kernel methods such as kernel linear discriminant analysis and kernel principal component analysis.