Adaptive graph regularization non-negative matrix factorization method for face recognition

A non-negative matrix decomposition, face recognition technology, applied in the field of adaptive graph regularization non-negative matrix decomposition, can solve the problems of low accuracy of face recognition, damage to local neighborhood structure information, etc., to remove noise and outliers Value, local neighborhood structure information is complete, and the effect of improving the recognition accuracy

Active Publication Date: 2019-04-19
HENAN UNIV OF SCI & TECH
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Problems solved by technology

[0007] In order to solve the problem that the original data for face recognition in the prior art usually contains a lot of noise and outliers, which may cause the destruction of the local neighborhood stru

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  • Adaptive graph regularization non-negative matrix factorization method for face recognition
  • Adaptive graph regularization non-negative matrix factorization method for face recognition
  • Adaptive graph regularization non-negative matrix factorization method for face recognition

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Embodiment Construction

[0024] The present invention will be further described below in conjunction with the accompanying drawings.

[0025] The processing procedure of the non-negative matrix factorization (NMF) described in the present invention is: face photo all carries out matrix processing, obtains data matrix X, X=[X 1 ,X 2 ,...,X n ]∈R m×n , where X i (i=1,2,...n) represents i data points, each data point is an m-dimensional vector, and there are n data samples in total.

[0026] For NMF, the aim is to find the product of two non-negative matrices to represent the original data matrix.

[0027] X≈UV T (1)

[0028] where U ∈ R m×k is the basis matrix, V∈R n×k is the coefficient matrix. When clustering, set k as the number of clusters, and each column of matrix U is a basis vector. Based on the basis matrix U, any data point is reconstructed from the data X by using different linear combinations of these k-column vectors in V. Each row vector of matrix V is the weight coefficient of...

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Abstract

The problem that in the prior art, the face recognition accuracy is low is solved. The invention provides an adaptive graph regularization non-negative matrix factorization method for face recognition. The method comprises the following steps: vectorizing a face picture; decomposing the data matrix X into a product of a base matrix and a coefficient matrix; adding a non-negative constraint to eachelement in the basis matrix and the coefficient matrix; constraining a coefficient matrix of the data matrix X; obtaining a data matrix X, obtaining a processed similarity matrix, processing the similarity matrix S by utilizing a diagonal matrix and a Laplace matrix, introducing a weight constraint item Lambda to obtain an adaptive graph, and finally introducing the adaptive graph as a regularization constraint item into a non-negative matrix factorization objective function of the data matrix X to identify the face photo. According to the method, the weight constraint term is introduced, theweight matrix is not kept unchanged any more, a large amount of noise and outliers are removed, local neighborhood structure information is complete, and the recognition accuracy is improved.

Description

technical field [0001] The invention relates to the field of digital representation, in particular to an adaptive graph regularization non-negative matrix decomposition method for face recognition. Background technique [0002] With the development of science and technology in the real world, various devices generate a large amount of data, and face recognition technology is becoming more and more common. These data need to be analyzed and processed to obtain useful information. However, data usually has high dimensionality and a lot of redundancy. These data will cause a series of problems in practical applications, such as long calculation time and large space required. Therefore, how to effectively deal with these high-dimensional data has become a hot issue. Feature selection can solve this problem very well. Feature selection requires one to select useful subsets from a high-dimensional feature space for subsequent processing. Feature selection can be roughly divide...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/172G06F18/2133G06F18/23
Inventor 刘中华张琳王京京王晓红邱涌肖春宝
Owner HENAN UNIV OF SCI & TECH
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