Research On Face Recognition Technologies Based On Kernel Feature Extraction Methods

Face recognition is an important branch of biologic feature identification.Because of its advantages comparing to other biologic Features.Of all thebiometric features,the face is so common and reachable that face recognition remains one of the most active research issues in pattern recognition and image processing.Feature extraction is the elementary problem in the area of pattern recognition.For face recognition tasks, extraction of effective image features is the key step. Kernel-based feature extraction methods are a very effective nonlinear feature extraction method.From the early geometry based methods to statistics based methods.Although linear techniques have been fully developed,they are still inadequate to describe the complexity of real face images because of the illumination,facial expression and pose variation.Hence it's necessary to extend the linear techniques to the nonlinear ones. Kernel Feature extraction Methods is a very effective nonlinear Feature extraction Methods.This paper has do good research on Feature extraction Methodswhich applied in face recognition.The proposed algorithm has obtained a good recognition result on the FERET person face database experiment.The followings are my main work:(1)Feature extraction is one of the most essential problems in pattern recognition.Kernel Fisher discriminant analysis (KFDA)is thoroughly studied in the paper.A equivalent but more simple nonlinear feature extraction method is found.The main idea of this method is that the original input space is transformed into a lower dimensional feature space.On this basis, a general model for feature extraction is proposed,by which, a matrix similarity based feature extraction algorithm is developed.Finally,the experimental results on Yale indicate that the proposed algorithm is effective.(2)A Two-stages kernel feature extraction methods for Face Recognition is developed in this paper.The algorithm includes two stages:firstly,the classical principal component analysis(C-PCA)is employed to condense the dimension of image vector.What follows:kernel Fisher discriminant analysis(KFDA) or KPCA are applied to the reduced dimensional training samples. On this basis,a more efficient method,called I-PCA+KFDA or I-PCA+KPCA are proposed.Finally,The experimental results on ORL face databases indicate that the proposed methods is more efficent than KFDA while retaining the same recognition.(3)The advantage of a kernel method often depends critically on a proper choice of the kernel function.A promising approach is to learn the kernel from data automatically.This paper propose a novel method for learning the kernel matrix based on maximizing a class separability criterion that is similar to those used by linear discriminant analysis(LDA)and kernel Fisher discriminant(KFD).But,we may note that optimizing this criterion function does not require inverting the possibly singular within-class scatter matrix which is a computational problem encountered by many LDA and KFD methods.