PCA and LDA based fuzzy face recognition system

The paper proposes a fuzzy face recognition system based on the integration of principal component analysis (PCA) and linear discriminant analysis (LDA). It aims to find out the eigenvalues, eigenvectors, and eigenspace of human facial features using PCA firstly, and then obtain the data of facial weightings by projecting the eigenvalues to eigenspace of human face. The purposes of integrating LDA to the PCA based fuzzy recognition scheme are not only to reduce the dimension of the images, but also to reduce the level of the image isolation in different categories by LDA to expend the distances between each central point of different categories. After these, one can determine the magnitude of Euclidean distance by a fuzzy scheme to make the recognition decision of human faces. These will accomplish fine and successful facial recognition.