Face Detection Research Based On Kernel Function

Human face automatic recognition system is a cross subject combined with pattern recognition, computer vision and biometrics, in which human face detection is a key factor.It is generally believed that there is no more important problems than the nonlinearity of examples from actual space and dimensionality reduction of examples space during the course of the research of human face recognition. As a key factor of the success of the support vector machine, a more novel theory and more valid method to deal with such problems as nonlinearity of swatch and dimensionality reduction, kernel function theory has attracted sweeping researchers to apply it to existing pattern recognition fields and develop itIn the first part, the research actuality of human face detection technology is summarized. The traditional methods for feature extraction and classification are enumerated and compared with each other, which can not give attention to two or morethings such as nonlinearity and little examples learning.In the second part, beginning from the analysis about the limitation and artificiality of Artificial Neural Network, the support vector machine (SVM) is introduced and described, of which the convex quadratic programming and kernel function lead to the success. Then the statistic learning theory and the hypostasis of the kernel function are discussed. Then a series of kernel-methods based on thekernel function for classification and discriminat are studied and developed such as Kernel-based Principal Component Analysis (KPCA), Kernel-based Fisher Discriminat, of which the emphases are the principal, function constructing, parameters of the kernel functions. The arithmetic above are emulated by MATLAB. At last, a novel ameliorated human face detection algorithm based on kernel function is proposed.In the third part, a face detection and validation algorithm based on kernel function is proposed, in which the ameliorated SVM is used to perform face detection and a trained KPCA to face validation.In the last part, the results of the above-mentioned emulation experiment is provided, which are classified, compared and deduced It is proved that the human face recognition system based on kernel function have outstanding little swatch learning ability and very valid to deal with nonlinearity and dimensionality reduction which the common methods can not solve such as Linear Fisher Discriminant, model matching and Eigenface.