The Research Of Feature Extraction Methods And Their Application To Face Recognition

Face recognition is a biological recognition technology with great developable potential, which is believed to have a great deal of potential applications in information security, public security and financial security. In face recognition, feature extraction is one of the key steps. In the passed decade years, many correlated algorithms have been proposed to solve this problem. Linear feature extraction methods, such as Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA) and Locality Preserving Projections (LPP), are developed to solve linear problem, and nonlinear feature extraction methods, such as kernel function methods based on support vector machine (SVM), are proposed to solve nonlinear problem. In this paper, linear and nonlinear methods on feature extraction field are both deeply analyzed. Not only are effectiveness and performance considered in our proposed methods, but the propsed methods can effectively overcome small size samples problem. The researchs of the dissertation are:(1) The uncorrelated discriminant analysis is a effective method for feature extraction, but it may encounter the small size sample problem when this method is applied in face recognition task. This algorithm, which is solved by recursive method, has low speed of computation. Feature extraction methods based on image matrix model can effectively solve the small sample size problem, so a new algorithm based on image matrix model is proposed, which is called two-dimensional uncorrelated discriminant vectors. Being based on image matrix model, the new algorithm avoids small sample sizes problem. Through whitening transform of within-class scatter matrix, uncorrelated discriminant vectors can be obtained non-recursively.The new method computes faster while maintaining numerical stability.(2) The uncorrelated discriminant analysis based on image vector model is deeply analyzed. An improved uncorrelated space method is proposed. The main idea of the proposed algorithm is to map the original space into a low dimensional subspace, and then the singularity of the total-scatter matrix can be avoided in this low dimensional subspace. In addition, according to the symmetry of scatter matrix, a fast method is introduced in order to further improve speed of computing uncorrelated discriminant vectors. The new method not only effectively solves the small size sample problem but also computes faster.(3) Kernel method as a non-linear dimension reduction method is widely used. The existed results show that the feature extraction methods based on the kernel mapping outperform the original linear feature extraction methods. Three algorithms, namely orthogonal discriminant locality preserving projections, discriminative common vectors and kernel uncorrelated space method, are effective methods for feature extraction, but they are linear feature extraction methods. Therefore three nonlinear feature extraction methods are developed in the paper, namely kernel orthogonal discriminant locality preserving projections, kernel discriminative common vectors and kernel uncorrelated space method. To three nonlinear feature extraction methods through the ingenious transformation, they are realized in the process only to calculate the inner product of the input samples, not only avoid the algorithm computation complexity, simultaneously also effectively enhance the algorithm recognition performance.(4) A kernel feature extraction optimization model based on the compression transformation for small sample sizes problem is developed in the paper. Firstly, according to the fisher criterion, the original samples are compressed into a lower dimensional space, then the kernel feature extraction algorithms are implemented on the compressed samples. The optimized methods guarantee recognition performance of the original methods, simultaneously save the computation load and the memory expenses, also enhance the algorithms usability.