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

Block Face Recognition Method Based on Weighted Intensity PCNN Model

A technology of face recognition and segmentation, applied in character and pattern recognition, instruments, computing, etc., can solve problems such as ignoring faces and insufficiently detailed image feature descriptions

Active Publication Date: 2018-07-10
谷德智能科技研究院(山西)有限公司
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The present invention solves the problem that the image feature description in the existing face recognition method based on the PCNN model is not detailed enough and uses a set of parameters to process the entire face image indiscriminately, ignoring the different parts of the face, and provides A Face Recognition Method Based on Weighted Intensity PCNN Model

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
  • Block Face Recognition Method Based on Weighted Intensity PCNN Model
  • Block Face Recognition Method Based on Weighted Intensity PCNN Model
  • Block Face Recognition Method Based on Weighted Intensity PCNN Model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0090] The block face recognition method based on the weighted strength PCNN model is realized by the following steps:

[0091] (1) Training stage

[0092] Construct or select an existing face library containing A person, B face images for each person, and a total of A*B face images; take the first N faces of each person in the face library, N≤B / 2, The face image is used as the training image, and each training image is horizontally divided into blocks according to the eyes, nose and mouth, and divided into upper, middle and lower block diagrams corresponding to the eyes, nose and mouth respectively. The three block images of each face image are input into the weighted intensity PCNN model, and each block will get a weighted intensity matrix, so that A*N*3 weighted intensity matrices will be obtained, which correspond to each person's Each block of the training images, and then calculate the average value of the N weighted intensity matrices corresponding to the upper, middle...

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 relates to a face recognition method based on a PCNN model, in particular to a block face recognition method based on a weighted strength PCNN model. It solves the problem that the image feature description in the existing face recognition method based on the PCNN model is not detailed enough and uses a set of parameters to process the entire face image indiscriminately, ignoring the different parts of the face. On the basis of simplifying the PCNN model, the present invention proposes a weighted strength PCNN model, introduces the concepts of spontaneous pulse emission strength, coupling pulse emission strength and weighted strength, and refines the output of the model. At the same time, block recognition is used for face recognition. Before recognition, the face image is divided into blocks according to the difference in the gray distribution of each part of the face image and the difference in the local recognition rate. When performing face recognition, the weight of the block will be adaptively set according to the block image; the final recognition result of a face image will synthesize the recognition results of each block.

Description

technical field [0001] The invention relates to a face recognition method based on a PCNN model, in particular to a block face recognition method based on a weighted strength PCNN model. Background technique [0002] The pulse-coupled neural network was discovered by Eckhorn et al. when studying the synchronous pulse emission phenomenon of the brain visual cortex of cats, monkeys and other animals, and was improved by Johnson in 1993, and the standard pulse-coupled neural network (Pulse Coupled Neural Network) was proposed. Neural Network PCNN) model. In 1999, Lzhikevich strictly proved that the PCNN model is a network model closest to biological neurons from a mathematical point of view. Compared with the previous classical neural network, the PCNN model is a self-supervised and self-learning network, which can realize image processing without training, and the PCNN model has excellent comprehensive spatio-temporal summation characteristics, dynamic synchronous pulse emiss...

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): G06K9/00G06K9/62
CPCG06V40/16G06F18/2155
Inventor 邓红霞李海芳郭浩相洁曹锐李瀚杨晓峰
Owner 谷德智能科技研究院(山西)有限公司
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