A Combined Kfda Method And Gui Realized For Face Recognition

Traditional face recognition methods such as Principal Components Analysis(PCA), Independent Component Analysis(ICA) and Linear Discriminant Analysis(LDA) are linear discriminant methods, but in the real situation, a lot of problems can't be linear discriminated; therefore, researchers proposed face recognition method based on kernel techniques which can transform the nonlinear problem of inputting space into the linear problem of high dimensional space. In this paper, we propose a recognition method based on kernel function which combines kernel Fisher Discriminant Analysis(KFDA) with kernel Principle Components Analysis(KPCA) and use typical ORL(Olivetti Research Laboratory) face database as our experimental database. There are four key steps: constructing feature subspace, image projection, feature extraction and image recognition. We found that the recognition accuracy has been greatly improved by using nonlinear identification method and combined feature extraction methods. We use MATLAB software as the platform, and use the GUI to demonstrate the process of face recognition in order to achieving human-computer interaction and making the process and result more intuitive.