Face recognition using regularized Kernel Direct Discriminant analysis Algorithms
Yang Jia-hong · Computer Engineering and Applications Journal · 2007
Traditional methods,such as PCA(Principle Component Analysis) and LDA(Linear Discriminant Analysis),not only suffer from the so-called Small Sample Size(SSS) problem,but also are insensitive to the high order relations of image pixels. In this paper,we propose kernel machine based regularized discriminant analyzing method,which projects the image space to the high dimensional feature subspace through some non-linear transformation,and then performs discriminant analysis using the kernel-skills on the new feature subspace.Extensive experiments on ORL database indicate the method proposed outperforms the traditional PCA,KPCA,LDA methods on feature extraction.It can also simplify the classifier design,meanwhile,accomplish high recognition rate.