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

Single-training sample face recognition method based on blocking consistency LBP (Local Binary Pattern) and sparse coding

A face recognition and sparse coding technology, applied in the field of face recognition, can solve the problems of easy confusion, algorithm loss of original information, poor recognition rate and robustness, etc., to avoid errors, high recognition rate and robustness. Effect

Inactive Publication Date: 2012-11-28
SHANGHAI JILIAN NETWORK TECH CO LTD
View PDF3 Cites 51 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0018] The purpose of the present invention is to provide a face recognition method based on block LBP and sparse coding in order to solve the above two confusing problems, as well as the problems of traditional algorithms losing original information, recognition rate and poor robustness

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
  • Single-training sample face recognition method based on blocking consistency LBP (Local Binary Pattern) and sparse coding
  • Single-training sample face recognition method based on blocking consistency LBP (Local Binary Pattern) and sparse coding
  • Single-training sample face recognition method based on blocking consistency LBP (Local Binary Pattern) and sparse coding

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0043] The specific implementation of the face recognition method based on block LBP and sparse coding of the present invention will be explained below in conjunction with the accompanying drawings, but it should be noted that the implementation of the present invention is not limited to the following embodiments.

[0044] A face recognition method based on block LBP and sparse coding. Firstly, the face image is divided into blocks to count the LBP histogram, and then the consistent LBP histogram is counted to obtain the feature vector corresponding to the entire image, and then the face is produced. The image training set matrix represents the test image as a linear combination on the training set, and finally solves the sparsest solution of the linear combination coefficient vector x.

[0045] Concrete operational steps of the inventive method are as attached figure 1 shown.

[0046] 1. Block statistics LBP histogram

[0047] First, the face image is divided into grids ac...

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 belongs to the technical field of digital image processing and mode recognition, in particular to a face recognition method based on blocking consistency LBP (Local Binary Pattern) and sparse coding. The face recognition method comprises the steps of: firstly, segmenting a face image into 16 subdomains which are same in size according to a mode of 4*4, calculating a consistency LBP histogram with one pixel radius and 8 neighbors, connecting LBP histograms of the 16 subdomains into a column vector to be used as a characteristic vector of a face image; and representing images to be tested into a most sparse linear combination on a training set, and recognizing the face image. Compared with the traditional characteristic extraction and clustering algorithm. According to the invention, structure information of a face can be well extracted, and under the condition of a single training sample and shielding, higher recognition rate and robustness are shown.

Description

technical field [0001] The invention belongs to the technical field of digital image processing and pattern recognition, and in particular relates to a face recognition method. Background technique [0002] The biological characteristics studied by biometric identification technology include face, fingerprint, palm print, palm type, iris, retina, vein, voice (speech), body shape, infrared temperature spectrum, ear shape, smell, personal habits (such as typing on the keyboard) strength and frequency, signature, gait), etc., the corresponding recognition technology includes face recognition, fingerprint recognition, palmprint recognition, iris recognition, retina recognition, vein recognition, voice recognition (identification can be carried out by voice recognition, or For the recognition of voice content, only the former belongs to biometric recognition technology), body shape recognition, keyboard tapping recognition, signature recognition, etc. Face recognition specifical...

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
Inventor 董文彧郭跃飞蒋龙泉鲁帅冯瑞
Owner SHANGHAI JILIAN NETWORK TECH CO LTD
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