Human face recognition method and system based on kernel non-negative matrix factorization, and storage medium

Abstract

The invention discloses a human face recognition method and system based on kernel non-negative matrix factorization, and a storage medium. The method comprises the steps: constructing a fractional order inner product kernel function, wherein the fractional order inner product kernel function exerts no limit on a power exponent parameter; obtaining a fractional order inner product kernel non-negative matrix factorization algorithm through the combination of the fractional order inner product kernel function and the kernel non-negative matrix factorization; and carrying out the human face recognition through the fractional order inner product kernel non-negative matrix factorization algorithm. The method solves a problem that the power parameter of a polynomial kernel function just can be an integer, and enables the selection of the power parameter to be more flexible. The method effectively irons out the changes of posture and illumination in human face recognition. Moreover, the method is quick in convergence and excellent in recognition performance.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and relates to a face recognition method, system and storage medium based on kernel non-negative matrix decomposition. Background technique [0002] With the rapid development of social informatization and networking, face recognition has become one of the hottest research topics in the field of pattern recognition and image processing, and it is also one of the most successful applications of image analysis and machine vision. Face recognition technology is convenient, reliable and safe. It is a relatively widely accepted biometric method, and it plays a very important role in national security, social economy, home entertainment and other fields. [0003] In the rapid development of face recognition technology, many face recognition algorithms have been proposed one after another, the representative ones are Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local ...

AI Technical Summary

Problems solved by technology

[0006] 1. Non-negative matrix factorization algorithm (NMF) is a classic linear method in face recognition, but it often cannot effectively deal with face image data that is nonlinearly distributed due to changes in pose and illumination in face images

[0007] 2. Polynomial kernel non-negative matrix factorization (PNMF) is a nonlinear face recognition method, but it needs to converge under strong conditions, and the convergence speed is slow

In addition, the power exponent parameter of the polynomial kernel function can only be an integer, and when the power exponent parameter is a fraction, it cannot be guaranteed that it is still a kernel function

[0008] 3. Quadratic polynomial kernel non-negative matrix factorization (PKNMF) is also a nonlinear method, but it cannot theoretically prove the convergence of its iterative algorithm. In addition, its power index is fixed (d=2), and the power parameter cannot be Adjustment, that is, its parameters are less adjustable

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