Feature extraction optimizing method based on image figure face expression recognition

A facial expression and feature extraction technology, which is applied to the acquisition/recognition of facial features, character and pattern recognition, gene models, etc., can solve the problems of premature convergence and excessive data volume, so as to solve premature convergence and improve accuracy , the effect of reducing the number and dimension

Inactive Publication Date: 2018-08-24
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

This method combines the improved principal component analysis method with the particle swarm optimization algorithm integrated with the genetic algorithm, which solves the problem of detailed extraction of the facial expressions of the characters in the image, and avoids the problem of excessive data volume and premature convergence in the extraction process. question

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  • Feature extraction optimizing method based on image figure face expression recognition
  • Feature extraction optimizing method based on image figure face expression recognition
  • Feature extraction optimizing method based on image figure face expression recognition

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Embodiment Construction

[0036] Some embodiments of the accompanying drawings of the present invention are described in more detail below.

[0037] In specific implementation, figure 1 It is a flow chart of principal component feature extraction for facial expression images of people. First, set the input facial image with a size of 30*20 as matrix A. X is a 30-dimensional column vector representing the projection axis, and the linear change of Y=AX is directly projected onto X to obtain a 30-dimensional column vector Y representing the projection feature, and the optimal projection axis X opt It can be determined according to the divergence distribution of the feature vector Y. Use the trace of the covariance matrix Sx of the training sample to the projection feature vector Y to represent the optimal projection direction function, that is, J(x)=tr(Sx), wherein the training sample image comes from an existing image database for a given facial image The mapping, the covariance matrix is ​​expressed ...

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Abstract

The invention discloses a feature extraction optimizing method based on image figure face expression recognition. The method includes the following steps: inputting a face image and representing the face image in the form of a matrix, and obtaining projection characteristic vectors after axes of projection are arranged; setting a projection characteristic covariance matrix of a given image, and using the track of the matrix to represent an optimal projection direction function; updating the optimal projection direction function on the basis of an overall distribution matrix of a training sample image, wherein a vector set formed by the axes of projection satisfying the maximum value of the function updates the projection characteristic vectors and forms a matrix representing expression characteristics; endowing each element in the characteristic matrix with a weight, and optimizing the optimal projection direction (that is to say, optimizing a global optimal solution) through a particle swarm algorithm modified by Gaussian mutation; and dividing the global optimal solution in the particle swarm algorithm into secondary groups including leaders and followers, and carrying out secondary optimization and recognition on multiple main parts representing image figure face expressions. According to the invention, expression characteristic results of a figure face can be effectively distinguished and optimized.

Description

technical field [0001] The invention relates to a feature extraction and optimization method based on facial expression recognition of image characters, which mainly utilizes a two-dimensional principal component analysis method based on statistical feature extraction and an improved particle swarm algorithm to optimize the solution of an image matrix, belonging to image processing, pattern recognition and computer Visual intersection technology application field. Background technique [0002] The purpose of the feature extraction optimization method is to avoid premature convergence and reduce the accuracy of facial expressions when extracting main features. The recognition of facial expressions of characters based on images has become a hot issue in the application of artificial intelligence, and it plays an important role in the analysis of real-time emotional changes of characters in computer vision. There are three main methods for extracting facial expression features...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/00G06N3/12
CPCG06N3/006G06N3/126G06V40/174G06V40/168G06F18/2135
Inventor 陈志刘玲岳文静周传陈璐掌静
Owner NANJING UNIV OF POSTS & TELECOMM
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