According to statistical learning theory

feature selection is realizable by valid heuristic search stage

which minimizes an estimated generalization error or some other related performance measure of SVM. In this paper

we introduce a facial features selection method. The optimal features subset is selected by minimizing VC leave-one-out (LOO) error or span error estimate of support vectors

which are regarded as the feature selection criterion of wrapper approach

through recursive feature elimination (REF). Face recognition is essentially a pattern recognition problem with typical small-sample size in sparse hyper-high dimensional space. The basic or most important part is how to obtain the significant features for classification. Theoretically

combing feature selection with classification model design outperforms traditional feature extraction or feature selection methods. Therefore

we propose a novel framework of the facial features selection based on filter (WT+KPCA) and wrapper (minimizing generalization error estimation) approaches. Experimental results on UMIST face database indicates that the proposed feature selection framework is time efficiency and has a significant improvement on the classification accuracy.