FACIAL RECOGNITION METHOD BASED ON KERNEL DISCRIMINANT ANALYSIS

摘要

A facial recognition method based on kernel discriminant analysis. The method comprises: performing characteristic extraction, mapping sample data to a high-dimensional kernel space, and performing a first characteristic extraction using a principal component analysis method; constructing a centering matrix H and solving a characteristic equation; calculating a vector; extracting a principal component to complete the first characteristic extraction to obtain a sample Y retained after the principal component analysis of facial data; performing a second characteristic extraction on Y using a linear discriminant analysis method; projecting a test data set X' to a characteristic subspace of a training set to obtain a test data set Z' after characteristic extraction; and classifying and recognizing the sample Z' by means of a nearest neighbor classifier. The facial recognition method based on kernel principal component analysis can significantly shorten the recognition time. The application of a kernel method can remedy the defect that nonlinear information in data cannot be utilized in the principal component analysis method and the linear discriminant analysis method.