Face recognition construction method and system based on non-linear non-negative matrix factorization and storage medium

Abstract

The invention provides a face recognition construction method and system based on non-linear non-negative matrix factorization and a storage medium. The present invention has the following advantages:1. by using norm substitution F to measure the loss degree of matrix factorization, the problem that the existing kernel non-negative matrix factorization algorithm is sensitive to the singular value is solved, and the stability of the kernel non-negative matrix factorization algorithm proposed by the invention is enhanced. 2. The sparsity of the feature representation of the algorithm of the present invention is enhanced by adding a regularization restriction on the coefficient matrix H into the objective function. 3. By integrating the kernel non-negative matrix factorization with new objective function and fractional inner product kernel function, a sparse fractional inner product nonlinear non-negative matrix factorization algorithm with high performance is obtained, which solves theproblem that the super parameters of polynomial kernel function can only be integers, and improves the discriminant ability of the algorithm.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a non-linear non-negative matrix factorization face recognition construction method, system and storage medium. Background technique [0002] With the advent of the information age, biometric technology, which uses the inherent physiological and behavioral characteristics of the human body for personal identification, has become one of the most active research fields. Among the many branches of biometric technology, the most easily accepted technology is face recognition technology, because compared with other biometric technologies, face recognition is non-invasive, non-mandatory, and non-contact. and concurrency. [0003] Face recognition technology consists of two stages. The first stage is feature extraction, which is to extract the face feature information in the face image. This stage directly determines the quality of face recognition technology; the secon...

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Problems solved by technology

[0025] It can be seen from this: 1. The loss function of the current nonlinear non-negative matrix factorization algorithm is constructed based on the F-norm. 2. The power exponent of the polynomial kernel function in polynomial kernel non-negative matrix factorization (PNMF) can only be an integer, and when the power exponent is a fraction There is no guarantee that it is still a kernel function, and the discriminative ability of the kernel function is weakened when the exponent is an integer

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