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

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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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 structure information and thus reduce the accuracy of face recognition, the present invention provides a method using Adaptive Graph Regularization Non-Negative Matrix Factorization Method for Face Recognition

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