The Research Of Image Feature Extraction Methods And Its Application In Face Recognition

Feature extraction is the elementary problem in the area of pattern recognition. It is the key to solve the problems such as face identification and handwritten character recognition. In this paper, we focus our attention on linear and nonlinear feature extractions and develop some new algorithms as regards it. And, these algorithms are verified to be effective in the application of image identification.Firstly, based on the Non-negative matrix factorization (NMF), a new algorithm of orthogonal projection axis and a new algorithm of statistically uncorrelated projection axis for feature extraction are proposed in this paper. Aim of the proposed methods is reducing or eliminating the statistical correlation between features and improving recognition rate. The experimental results on Olivetti Research Laboratory (ORL) face database and YALE face database show that the new methods are better than original NMF in terms of recognition rate.Non-negative matrix factorization (NMF) is an unsupervised feature extraction method in image recognition, meaning that NMF does not sufficiently use the class information of given training sample in feature extraction. A novel supervised feature extraction method based on non-negative matrix factorization is presented in this paper. The new method has two traits: one is to sufficiently utilize a given class label of training sample in feature extraction and the other is to still follow the same mathematical formulation as NMF, so the new feature extraction method is named class-information-incorporated non-negative matrix factorization (CINMF). Besides, in order to further improve recognition rate, the paper presents a new classification strategy based on fusion of two kinds of feature vector.Secondly, for nonlinear feature extraction, a novel supervised feature extraction method based on kernel principal component analysis (KPCA) is presented in this paper. The method is named as class-information-incorporated kernel principal component analysis (CIKPCA). As a nonlinear feature extraction, the conventional KPCA is an unsupervised method, it is not to sufficiently utilize a given class label information of training kernel sample in feature extraction, but CIKPCA overcomes the drawback. The paper presents a new classification strategy by fusion of two kinds of feature vector in order to further improve recognition rate. The experimental results show that the new method is better than KPCA in terms of recognition rate, and even outperforms KLDA. Besides, based on the (kernel) maximum margin criterion, new algorithms of statistically uncorrelated optimal (kernel) discriminant vectors for feature extraction is presented in this paper. The proposed methods have more powerful capability to eliminate the statistical correlation between features and improve efficiency of feature extraction.Lastly, based on manifold learning, a new unsupervised discriminant projection for dimensionality reduction of high dimensional data is presented in this paper. The new projection can be seen as a linear approximation of a multimanifolds-based learning framework which is based on both the local and nonlocal statistically quantities. The discriminant criterion function be characterized by difference between the nonlocal scatter and the local scatter, seeking to find a group of projection axis that simultaneously maximizes the nonlocal scatter and minimizes the local scatter of feature vector. Locality preserving projection (LPP) considers only the local scatter for classification . The experimental results on Olivetti Research Laboratory (ORL) face database and AR face database show that the proposed method consistently outperforms LPP and UDP, and even outperforms Fisher linear discriminant analysis (LDA).To avoid the complication of a singular local scatter matrix, we present a new feature extraction method by the idea of manifolds learning, the trait of the method is to exploit image matrixes to directly construct local scatter matrix and nonlocal scatter matrix. Its criterion function is characterized by maximizing the ratio of the nonlocal scatter to the local scatter after the samples are projected. The advantage of this new approach is that the reduction in dimensionality can be achieved in both row and column directions; the method is called the two-directional two-dimensional unsupervised discriminant projection (i.e. (2D)~2UDP). The experimental results on ORL databases and AR databases indicate that the new method is the highest among LPP, PCA, (2D)~2PCA and (2D)~2UDP in terms of recognition rate.