Face recognition method based on discriminative non-convex low-rank decomposition and superposition linear sparse representation

A low-rank decomposition, face recognition technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of insufficient training samples and incomplete dictionaries, so as to increase incoherence and remove inter-class correlation. , the effect of suppressing intra-class variation

Active Publication Date: 2019-07-30
HANGZHOU DIANZI UNIV
View PDF5 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The face recognition algorithm based on Sparse Representation (SRC) forms an over-complete dictionary according to the training samples, so as to establish a linear expression for the test samples, and complete the cla

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 method based on discriminative non-convex low-rank decomposition and superposition linear sparse representation
  • Face recognition method based on discriminative non-convex low-rank decomposition and superposition linear sparse representation
  • Face recognition method based on discriminative non-convex low-rank decomposition and superposition linear sparse representation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0015] The present invention will be further described below in conjunction with the drawings.

[0016] The face recognition method of superimposed linear sparse representation based on discriminative non-convex low-rank decomposition includes the following steps:

[0017] Step 1. According to the theory of low-rank matrix decomposition, replace the kernel norm with γ norm for low-rank matrix decomposition, and introduce structurally irrelevant discriminants to form discriminative non-convex low-rank decomposition;

[0018] Step 2. Use the alternating direction multiplier method to solve the discriminative non-convex low-rank decomposition, and decompose the face sample matrix into a low-rank matrix and a sparse matrix;

[0019] Step 3. The obtained low-rank matrix is ​​decomposed into a prototype dictionary and a mutation dictionary through superposition linear representation, and then the two dictionaries are combined by linear weighting as a dictionary for testing;

[0020] Step 4. U...

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 discloses a face recognition method based on non-convex low-rank decomposition and superposition linear sparse representation. The method comprises the following steps: 1, according to alow-rank matrix decomposition theory, replacing a nuclear norm with a gamma norm for low-rank matrix decomposition, and introducing a structure incoherent discrimination item to form discriminative non-convex low-rank decomposition; 2, resolving the discriminative non-convex low-rank decomposition, and decomposing the face sample matrix into a low-rank matrix and a sparse matrix; 3, decomposing the low-rank matrix into prototype dictionaries and variation dictionaries through superposition linear representation, and then using the two dictionaries as dictionaries for testing through linear weighting combination; and 4, solving a sparse coefficient of l1 norm by using a homotopy method by using a sparse representation algorithm, carrying out classified recognition on the face images by reconstructing a minimum sparse residual model, and classifying the face samples to be tested into a class with a minimum error, thereby realizing face recognition. According to the method, good robustness and high efficiency can be maintained under the conditions of shielding and noise pollution.

Description

Technical field [0001] The invention belongs to the technical field of biometric identification and information security, and particularly relates to a face recognition method based on low-rank decomposition and sparse representation. Background technique [0002] Face recognition is an important topic in pattern recognition in the past two decades. It includes computer vision, pattern recognition and many other subjects, and it also has a very wide range of applications. As the application range becomes wider and wider, face recognition is being valued by more and more important national institutions and enterprises. Now, face recognition has shown its application value in many fields such as related documents and various financial card identity verification, various security verification systems. [0003] Face recognition is to use a computer to find a face image in a single static face image or dynamic video, and then use related algorithms to extract feature information that i...

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): G06K9/00
CPCG06V40/172G06V40/161
Inventor 罗宵晗叶学义王鹏陈泽陈华华
Owner HANGZHOU DIANZI 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