Methods Of Feature Extraction Based On Manifold Learning In Face Recognition

Face recognition, characterizing by its naturalness, directness, non-contact,securit, etc., has developed to be a most potential biometric identification technology.The effective information of the facial features is utilized for personal identification.Face recognition has tremendous using value in authentication and identify occasions,and can promote the development of pattern recognition and many other subjects.Therefore, the study of face recognition technology is great theoretical and practicalsignificance.Extract effective discriminant features is a key factor in face recognition, whichrequires reduction of the data dimensionat and keep the face data set of the originalnature of structural characteristics unchanging at the same time. As non-lineardimension reduction methods, manifold learning can effectively learn the intrinsicgeometry of the high-dimensional manifold data structure closely related to highnonlinearity and properties. In this paper, based on the deeply inveatigation of featureextraction method based on manifold learning, the main contents and innovations arelisted as follows:Firstly, on the basis of Neighborhood Preserving Discriminant Embedded (NPDE),introducing the idea of Kernel mapping, finding the optimal projection vector usingSchur orthogonal way while solving the eigenvalue, proposing the Kernel OrthogonalNeighborhood Preserving Discriminant Embedding (KONPDE), overcoming theproblem that NPDE is hard to extract nonlinear characteristics, well maintaining theinformation of geometry and the discriminant of structural information of the facemanifold.Secondly, supervision algorithm and unsupervised algorithm can not make fulluse of limited training samples. Therefore, the Unsupervised Discrimination Projection(UDP) and Marginal Fisher Analysis (MFA) are combined to improve to besemi-supervised algorithm. There among, a large number of label-free samples arestudied using UDP, a small number of label samples are investigated using the MFA.At the same time, choose the maximum scatter difference criterion as the objectivefunction to avoid the divergence matrix singular value, verify the feasibility andeffectiveness of the method through theoretical and experimental alanalysis.Finally, the Tensor Marginal Fisher Analysis (TMFA) use image dimensionalityreduction to avoid that the traditional method expands the image in the form of a one-dimensional vector, which is effectively keep the face structure information.However, when building a nearest neighbor diagram, TMFA employ the global unifiedk-neighborhood method to select a close neighbor of the point, which is more difficultfor non-uniform flow shape processing. On the basis of the above algorithm in theresearch, the method that adopting the relationship between the euclidean distance andthe geodesic distance to select training samples nearest neighbor points dynamically isproposed, which makes it more effective to select local linear or nearly linear regionfor each sample.