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

Human face emotion recognition method in complex environment

A technology for emotion recognition and complex environments, applied in the field of face recognition, can solve problems that are difficult to industrialize, and achieve high efficiency, reliability, and high accuracy

Inactive Publication Date: 2017-12-01
深圳帕罗人工智能科技有限公司
View PDF0 Cites 77 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the face emotion recognition method developed by any single technology above is difficult to evolve into a technical solution on the basis of a mobile embedded computing platform, so it is difficult to industrialize

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
  • Human face emotion recognition method in complex environment
  • Human face emotion recognition method in complex environment
  • Human face emotion recognition method in complex environment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0022] In order to express the present invention more clearly, the present invention will be further described below in conjunction with the accompanying drawings.

[0023] figure 1 As shown, it is the overall flow chart of the face emotion recognition method based on the complex environment realized by the present invention. The flow of face expression recognition is as follows: first input video pictures, then perform face detection, and preprocess the detection results , to remove interference (de-interference processing), and then perform line edge mapping for face area recognition and face area selection, and perform Gabor wavelet transformation on 8 regions of interest for the mapped data, and uniformly transform the transformed image The LBP feature extraction of the pattern, and then through the PCA principal component analysis to reduce the dimensionality of the extracted features, compare the feature vectors, compare the motor unit and the neutral expression, and fin...

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 human face emotion recognition method in a mobile embedded complex environment. In the method, a human face is divided into main areas of a forehead, eyebrows and eyes, cheeks, a noise, a month and a chain, and 68 feature points are further divided. In view of the above feature points, in order to realize the human face emotion recognition rate, the accuracy and the reliability in various environments, a human face and expression feature classification method is used in a normal condition, a Faster R-CNN face area convolution neural network-based method is used in conditions of light, reflection and shadow, a method of combining a Bayes Network, a Markoff chain and variational reasoning is used in complex conditions of motion, jitter, shaking, and movement, and a method of combining a deep convolution neural network, a super-resolution generative adversarial network (SRGANs), reinforcement learning, a backpropagation algorithm and a dropout algorithm is used in conditions of incomplete human face display, a multi-human face environment and noisy background. Thus, the human face expression recognition effects, the accuracy and the reliability can be promoted effectively.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence mobile video image recognition, in particular to a face recognition method in a complex environment, which is an emerging field in the world at present. Background technique [0002] Face recognition based on video images is an emerging important research field in the world. Since NVIDIA launched the deep learning graphics processor graphics card (GPU) based on servers and computer hosts in 2006, the world's leading high-tech companies Such as Google, Apple, Amazon, well-known scientific research institutes such as MIT, Berkeley and Stanford, have adopted Nvidia's GPU graphics card to develop image recognition-based applications. In contrast, the global mobile embedded video image recognition market lags far behind similar server-based applications. It was not until mid-May 2017 that manufacturers including Nvidia began to provide high-performance machine vision and deep learning G...

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): G06K9/00G06K9/62G06N3/04G06N3/08G06T3/40G06T3/60G06T7/60
CPCG06N3/084G06T3/40G06T3/60G06T7/60G06T2207/20081G06V40/171G06V40/174G06N3/045G06F18/2411
Inventor 陈援东陈忠
Owner 深圳帕罗人工智能科技有限公司
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