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

A non-negative matrix decomposition and face recognition technology, applied in the field of data processing, can solve the problems of not being able to better resist changes in face recognition posture and illumination, weakening the discrimination ability of kernel functions, and sensitivity to outliers and sparseness, etc., to achieve The effect of enhancing sparsity, solving singular value sensitivity, and improving discrimination ability

Active Publication Date: 2018-12-14
SHENZHEN UNIV
View PDF6 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Face recognition construction method and system based on non-linear non-negative matrix factorization and storage medium
  • Face recognition construction method and system based on non-linear non-negative matrix factorization and storage medium
  • Face recognition construction method and system based on non-linear non-negative matrix factorization and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0056] Such as figure 1 As shown, the present invention discloses a construction method of nonlinear non-negative matrix factorization face recognition, specifically discloses a method based on l 2,p Modular nonlinear non-negative matrix factorization method for face recognition.

[0057] The construction method of the nonlinear non-negative matrix factorization face recognition comprises the following steps:

[0058] Loss degree characterization steps: use the l of the matrix 2,p - The norm characterizes the loss degree after matrix decomposition;

[0059] Sparsity enhancement step: Leverage the matrix l 1 -Norm enhances the sparse representation of features, and adds a regular term about the matrix H to the loss function;

[0060] Objective function composition step: through the loss degree characterization step and the sparsity enhancement step, the objective function F(W,H) is formed;

[0061] Steps to obtain the updated iterative formula of the fractional power inner...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/168G06V40/172G06F18/21322G06F18/21326G06F18/24
Inventor 陈文胜刘敬敏
Owner SHENZHEN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap