Research On Facial Feature Selection And Recognition Based On Support Vector Machine

Face recognition is essentially a typical small-sample pattern recognition problem in sparse hyper-high dimensional space. The key to solve the problem is how to obtain the significant features for classification. The criterion to evaluate the performance of feature selection lies on the ability of classification. A perfect feature selection method can make sure the selected feature subset obtain the best classification performance even adopt common classifiers. The aim of feature selection is to solve two problems. One is the criterion of feature selection, and the other is the strategy of feature search. Firstly, the dissertation gave a detailed analysis on the fundamental theory of SVM and corresponding core technologies-optimal theories, kernel function feature space and generalization theory. Secondly, the dissertation did research on the basic feature selection methods'charactristics (including Embedded, Filter and Wrapper feature seletion models), the features selection criterion and heuristic feature search method. The dissertation proposed to treat wavelet transform and KPCA as Filter feature selection model, and treat SVM as Wrapper feature selection model. Also, the dissertation proposed a whole new frame and corresponding solution on multiplex face feature selection and recognition method.The new solution proposed in the dissertation did a deep research on feature evaluation criterion of Wrapper feature selection model based on SVM.(1) The dissertation proposed a feature evaluation criterion that support vector contributions to classification depend on the support vector with margin minimization in SVM training, and applied it to face feature selection for the first time. So, we can obtain the optimized face feature subset in a certain classification threshold by GSFS feature search strategy. The experimental results on ORL, IITL and UMIST face databases demonstrate that the proposed algorithm has improved the performance on the reduction of feature dimensionality, classification recognition rate and the speed.(2) The dissertation proposed to treat radius/margin R 2 || w ||2 as evaluation criterion of Wrapper feature selection model, and applied it to face feature selection for the first time. The dissertation introduced feature selection via adaptive scaling base on SVM RFE, and this method would be useful to enhance the generalization of face recognition. The experimental results on UMIST face database demonstrate that the proposed algorithm could reduce feature dimensionality and enhance the classification...