Research On Feature Extraction Technology For Face Recognition

Face recognition is one of the hot topics in the field of pattern recognition, and it belongs to biometrics. In this field, feature extraction is one of the key steps. In the passed decade years, many correlated algorithms have been proposed to solve the problem. For example, linear discriminant analysis (LDA), principal component analysis (PCA) and independent component analysis (ICA) are developed to solve linear problem, and kernel methods based on support vector machine (SVM)) are proposed to solve nonlinear problem.The work in the paper includes:(1) In this paper, a new algorithm, called feature extraction based on symmetrical 2DPCA, is proposed. The algorithm is based on the theory of function decomposition in algebra and mirror symmetrical in geometry and 2DPCA.(2) In this paper, a new algorithm, called feature extraction based on complete fuzzy LDA, is proposed. The algorithm redefines the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix that make fully of the distribution of sample and simultaneously extract the irregular discriminative information and regular discriminative information.(3) In this paper, a new algorithm, called feature extraction based on laplacian MMC, is proposed. The algorithm defines the total laplacian matrix, within-class laplacian matrix and between-class laplacian matrix using the samples similar weighting to capture the scatter information of samples. Lapalcian MMC gets the discriminant vectors by maximizing the difference between between-class laplacian matrix and within-class laplacian matrix.(4) In this paper, a new algorithm, called feature extraction based on kernel unsupervised discriminant projection (Kernel UDP). We formulate the Kernel UDP theory and develop a two-stage method to extract Kernel UDP features.