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Method for automatically recognizing face expressions based on multi-characteristic fusion

A multi-feature fusion and facial expression technology, applied in the field of graphic recognition, can solve problems such as poor robustness, low recognition rate, and failure to consider the full utilization of local information and overall information.

Inactive Publication Date: 2017-04-26
HEBEI UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide an automatic facial expression recognition method based on multi-feature fusion, which is a method for fusing Gabor features and multi-scale ACILBP feature histograms of facial expression images and facial expression important region images, It overcomes the shortcomings of the existing facial expression recognition methods, which generally have poor robustness to illumination and noise, and do not consider the full use of local information and overall information, resulting in low recognition rates

Method used

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  • Method for automatically recognizing face expressions based on multi-characteristic fusion
  • Method for automatically recognizing face expressions based on multi-characteristic fusion
  • Method for automatically recognizing face expressions based on multi-characteristic fusion

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Embodiment 1

[0072] The facial expression automatic recognition method based on multi-feature fusion of the present embodiment is a method of fusing Gabor features and multi-scale ACILBP feature histograms of facial expression images and facial expression important region images, and the specific steps are as follows:

[0073] The first step is preprocessing of facial expression images and images of important areas of facial expressions:

[0074] (1.1) Geometric normalization of facial expression images:

[0075] Input the RGB image of the face into the computer through the USB interface, and use the formula (1) to convert it into a grayscale image O,

[0076] O(x,y)=0.299×R(x,y)+0.587×G(x,y)+0.114×B(x,y) (1),

[0077] Among them, R, G and B are the three channels of red, green and blue respectively, and (x, y) are the pixel coordinates of the image. For the obtained grayscale image O, the DMF_Meanshift algorithm is used to detect the key points of the face, and the eyes, The center poin...

Embodiment 2

[0126] In order to verify the advantages of the method of the present invention in the automatic recognition rate of human facial expressions, this embodiment selects six widely used facial expression recognition methods and compares them with the automatic recognition method of human facial expressions based on multi-feature fusion of the present invention, The six facial expression recognition methods are: Orthogonal Combination OfLocal Binary Patterns (OCLBP), Symmetric Local Graph Structure (Symmetric Local Graph Structure, SLGS), Noise-resistant Local Binary Pattern (Noise-resistant Local Binary Patterns, NRLBP), Strong Robust Local Binary Pattern (Completed Robust Local Binary Pattern, CRLBP), Local Mesh Patterns (LocalMesh Patterns, LMep), Joint Local Binary Patterns (JLBP).

[0127] Utilize the SVM classifier to carry out comparative experiments on the JAFFE and CK databases, wherein the selection mode of the training samples is random selection. In the present embodime...

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Abstract

The invention discloses a method for automatically recognizing face expressions based on multi-characteristic fusion, relates to a method for recognizing images. The method herein fuses images of face expressions, Gabor characteristics of images of major areas of face expressions and a multi-dimension ACILBP characteristic histogram. The method includes the following steps: pre-processing the images of the face expressions and the images of the major areas of the face expressions; separately extracting Gabor characteristics of the images of the face expressions and the images of the major areas of the face expressions, and imparting different weights, conducting fusion to obtain Gabor characteristics of two layers of images of the face expressions; using the ACILBP operator to extract the ACILBP characteristic histogram; conducting fusion to obtain the characteristic data of the face expressions; and using a SVM classifier to train and predict face expressions, and achieving automatic recognition of the face expressions. According to the invention, the method overcomes poor robustness under light and noise of prior art and the deficiency of low recognition rate due to insufficient utilization of partial information and whole information.

Description

technical field [0001] The technical solution of the present invention relates to a method for recognizing graphics, in particular to a method for automatic recognition of human facial expressions based on multi-feature fusion. Background technique [0002] Human language is divided into two categories: natural language and body language, and facial expressions are part of body language. Psychologists have found that when human beings communicate in a conversation: the language content accounts for 7%; the tone of speech accounts for 38%; and the speaker's expression accounts for 55%. Therefore, facial expressions play an important role in human communication activities. Corresponding expression recognition has always been a very active hotspot in the field of pattern recognition and computer vision. With the development of artificial intelligence and pattern recognition, facial expression recognition has received increasing attention, and its position in human-computer in...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/168G06V10/449G06V10/507G06F18/2411G06F18/253
Inventor 刘教民司浩强师硕刘依于洋阎刚郭迎春
Owner HEBEI UNIV OF TECH
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