Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Sparse characteristic face recognition method based on multilevel classification

A technology of face recognition and multi-level classification, applied in the field of image processing, to achieve the effect of reducing the amount of data, highlighting the ability of data compression, and wide application range

Inactive Publication Date: 2013-02-27
XIDIAN UNIV
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art, and propose a sparse representation face recognition method based on multi-level classification, to reduce the dimension of the face image, reduce the complexity of calculation, and improve the accuracy of the face recognition method in the number of face categories. In many cases, improve the recognition rate

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
  • Sparse characteristic face recognition method based on multilevel classification
  • Sparse characteristic face recognition method based on multilevel classification
  • Sparse characteristic face recognition method based on multilevel classification

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0021] refer to figure 1 , the implementation steps of the present invention are as follows:

[0022] Step 1. Randomly divide the face database used for training into n sub-databases, where n is 4, and use the Fisher criterion to achieve dimensionality reduction for the training face images in each sub-bank according to the following steps:

[0023] (1a) set the training face image sample set in the sub-library as: X={x i}, i=1, 2, ..., N, wherein, N is the total number of training face images in the sub-storey, the number of categories of training face images in the sub-storey is c, calculate the training face in the sub-storey The inter-class scatter matrix S of image samples b and the intra-class scatter matrix S w :

[0024] S b = Σ i = 1 c n i ( μ ...

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 sparse characteristic face recognition method based on multilevel classification, which mainly solves the defect that the traditional face recognition method can not effectively use multi-class face recognition. A realization process comprises the following steps of: (1) randomly dividing a face database for training into n sub-bases, respectively reducing the dimension of each sub-base, and retaining training face data after dimension reduction and a transformation matrix corresponding to each sub-base; (2) inputting a test face image, reducing the dimension of the test face image by using the transformation matrix of each sub-base, and retaining the test face data after dimension reduction; (3) carrying out inner-product operation by using the test face data after dimension reduction and training face data in each sub-base, using the front k sub-bases with a maximum inner product as candidate sub-bases, and reducing a searching range into the k sub-bases; (4) respectively recognizing faces in the k sub-bases and ensuring the classification of the test face images. Compared with the prior art, the invention is capable of effectively extracting face features and reducing computation complexity and is suitable for multi-class face recognition.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a face recognition method with sparse representation, which can be used for identity confirmation or search in the fields of public security criminal investigation, access control system, camera monitoring system and the like. Background technique [0002] Face recognition specifically refers to computer technology that uses analysis and comparison of facial visual feature information for identity identification. Face recognition is one of the most challenging research directions in the field of pattern recognition, machine learning and computer vision, and it is a high-dimensional pattern recognition problem. Therefore, people often extract features from face images and perform discrimination in low-dimensional subspaces. So far, various feature extraction methods have been widely used in the field of face recognition. [0003] Recently, Wright et al. proposed a new fac...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/66G06K9/00
Inventor 刘芳焦李成侯彪周挺戚玉涛王爽马文萍尚荣华郝红侠单雁冰
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com