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

A facial expression recognition method based on convolutional neural network

A convolutional neural network and facial expression recognition technology, applied in the field of facial expression recognition based on convolutional neural network, can solve the problems of low facial expression recognition efficiency, inaccurate positioning, complicated process, etc., to improve the recognition efficiency , the effect of changing the accuracy and reducing the complexity

Active Publication Date: 2021-08-10
LIAONING TECHNICAL UNIVERSITY
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the inaccurate positioning of the extracted feature points and the lack of effective feature points, the efficiency of facial expression recognition is low, and the process is relatively complicated.

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
  • A facial expression recognition method based on convolutional neural network
  • A facial expression recognition method based on convolutional neural network
  • A facial expression recognition method based on convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0046] like figure 1 As shown, the method of this embodiment is as follows.

[0047] Step 1, collect facial expression pictures through digital cameras, mobile phones or monitoring equipment, use the Internet to download FER-2013 face database and CK+ face database, obtain larger images about people's faces in order of magnitude, and divide the images into There are two parts of training set and test set.

[0048] Step 2. Preprocess the collected images, cut the collected images to a size of 96*96 pixels, place the face in the center of the image, and use matlab software to grayscale the color image in the face database After processing, a grayscale image of 96*96 size ...

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 human facial expression recognition method based on a convolutional neural network, and relates to the technical field of human facial expression recognition. This method first collects facial expression pictures, downloads the FER-2013 face database and CK+ face database, divides the images into training set and test set, and then preprocesses the collected images to obtain a grayscale image with a size of 96*96 , establish a convolutional neural network model, and use the training set for training, calculate the error between the actual output of the training and the label value, pass the difference from top to bottom through the back propagation algorithm, and use the weight update formula to update the weight value, after training, save the trained network model, input the images of the test set into the training model, and calculate the recognition rate. In the present invention, the facial expression recognition method is improved, the convergence speed of the model is improved, the recognition efficiency is improved, the accuracy rate of the convolutional neural network is changed, and the facial expression recognition efficiency is improved to a certain extent.

Description

technical field [0001] The invention relates to the technical field of facial expression recognition, in particular to a method for recognizing facial expressions based on a convolutional neural network. Background technique [0002] Facial expressions are an effective way to convey emotion. Expressions contain a lot of effective information about emotions; as a technology that can automatically identify faces, expression recognition has a high recognition efficiency for a single face image; due to the differences in the expressions of different people, the recognition rate is reduced . The expression recognition process is to reduce the existing differences through feature point extraction. However, due to the inaccurate positioning of the extracted feature points and the lack of effective feature points, the efficiency of facial expression recognition is low, and the process is relatively complicated. Facial expression recognition can be applied in many fields such as m...

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): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V40/171G06V40/174G06V10/467G06N3/048G06N3/045G06F18/24
Inventor 姜彦吉葛少成郭羽含王光杨帆
Owner LIAONING TECHNICAL UNIVERSITY
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