Facial expression recognition method based on feature extraction

A technology for facial expression recognition and feature extraction, which is applied in character and pattern recognition, facial feature acquisition/recognition, instruments, etc. It can solve problems such as fixed neighborhood size, inability to extract texture features, and operators being easily affected by noise. , to achieve the effect of good discrimination ability and good recognition effect

Pending Publication Date: 2021-07-23
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

LGBP has a stronger discriminative ability than LBP, but the operator is susceptible to noise, and

Method used

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  • Facial expression recognition method based on feature extraction
  • Facial expression recognition method based on feature extraction
  • Facial expression recognition method based on feature extraction

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Embodiment Construction

[0019] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0020] The design idea of ​​the present invention is to provide a method that can extract expression features at multiple scales and can comprehensively consider the pixel relationship between neighborhoods. figure 1 It is a schematic diagram of the 3×3 neighborhood grayscale of AR-LGBP. AR-LGBP operator neighborhood size is (2m+1)×(2n+1), where, w represents the image width, h represents the image height, m ​​is used to determine the width of any sub-neighborhood, n is used to determine the height of any sub-neighborhood, the symbol Indicates rounding down. This operator divides the neighborhood into 9 sub-neighborhoods, denoted as R i . These neighborhoods can be divided into four sub-neighborhoods R of size m×n 1 , R 3 , R 5 , R 7 , 2 subneighborhoods R of size m×1 4 , R 8 , 2 subneighborhoods R of size 1×n 2 , R 6 , ...

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Abstract

The invention discloses a facial expression recognition method based on feature extraction, which comprises the following steps: firstly, dividing a facial expression image into sub-regions, adopting two AR-LGBP operators with different sizes for the same pixel point to obtain two eight-bit binary sequences, carrying out logic exclusive-or operation on the two binary sequences in one-to-one correspondence to obtain a new binary sequence, and carrying out feature extraction on the new binary sequence; wherein a decimal numerical value converted from the sequence is the pixel value of the pixel point, calculating the pixel value of each pixel point in each sub-region according to the method to obtain a histogram of the sub-region, and connecting the histograms of the sub-regions to generate a facial expression feature vector; and finally, performing dimensionality reduction on the generated feature vector through a principal component analysis algorithm, and performing facial expression classification recognition in combination with an SVM classifier. According to the method, the pixel relation between neighborhoods is considered, the feature description capability can be improved, expansibility is achieved, and features can be extracted under different scales.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to a feature extraction technology with high efficiency, low complexity, good robustness and strong discrimination ability adopted in the expression recognition process. Background technique [0002] At present, the research on artificial intelligence has reached a high level, while the research on human emotion and cognition is relatively small. In real life, people expect computers to serve the society like human beings, and to be more intelligent in human-computer interaction, but it is far from enough to have the perception capabilities of vision and hearing, and it is necessary to add emotional understanding and emotional recognition functions . The visual information reflected by the human face is the most direct and important carrier of human emotional expression and interaction. Researchers can guess the true inner thoughts of the expresser through the chan...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/168G06V40/174G06F18/2135G06F18/2411
Inventor 郭晓金张哲张震刘煌
Owner CHONGQING UNIV OF POSTS & TELECOMM
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