Face Recognition Based on a Fast Kernel Discriminant Analysis Approach

Abstract

The computational cost of kernel discrimination is usually higher than linear discrimination, making many kernel methods impractically slow. To overcome this disadvantage, several accelerated algorithms have been presented, which express kernel discriminant vectors using a part of mapped training samples that are selected by some criterions. However, they still need to calculate a large kernel matrix using all training samples, so they only save rather limited computing time. In this paper, we propose the fast and effective kernel discriminations based on the mapped mean samples (MMS). It calculates a small kernel matrix by constructing a few mean samples in input space, then expresses the kernel discriminant vectors using MMS. The proposed kernel approach is tested on the public AR and FERET face databases. Experimental results show that this approach is effective in both saving computing time and acquiring favorable recognition results.