Face image dimension reducing method based on local correlation preserving

Abstract

The invention discloses a face image dimension reducing method based on local correlation preserving. The method comprises the following steps of: expressing a face image by using multi-dimensional vectors, acquiring k neighbors of each vector according to the norm of two difference vectors, and calculating normalization weight of the k neighbors of each vector according to a radial basis function; calculating a difference vector of each vector and the sum of the weights of the k neighbors of each vector, acquiring a matrix by multiplying transposition of each difference vector by each difference vector, and adding the matrixes corresponding all the vectors to acquire a local correlation preserving matrix; and calculating characteristic values and characteristic vectors of the local correlation preserving matrix, and selecting the characteristic vectors corresponding to partial large characteristic values as basic vectors to form a projection matrix, and thus realizing dimension reduction. The dimension reduced face image well preserves local data association, the method is beneficial to image identification, and the classification effect after characteristics are extracted by the method is superior to those of primal component analysis (PCA) and locality preserving projection (LPP); and calculation complexity is reduced, and a relation among the new method, the PCA and the LPP is disclosed.

Description

technical field [0001] The invention relates to a dimensionality reduction method of a human face image, in particular to a dimensionality reduction method of a human face image based on local association preservation. Background technique [0002] Face images are composed of a large number of pixel values, which are represented by high-dimensional vectors or high-order matrices. Face image recognition requires a lot of calculation and storage costs, resulting in the disaster of dimensionality. Therefore, before operating on face images, it is necessary to Image dimensionality reduction processing is to map the original face image to a low-dimensional space to obtain the most important features of the face image in the low-dimensional space, reduce calculation and storage costs, and realize automatic recognition of face images. [0003] At present, the classic dimensionality reduction method that does not consider the data category label is PCA (Primal Component Analysis: Pr...

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Problems solved by technology

The disadvantage of the above manifold learning method is that the calculation is complex, no explicit nonlinear transformation matrix is ​​given, and the transformation characteristics of the unlabeled face image cannot be obtained directly

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