Feature extraction optimizing method based on image figure face expression recognition

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...

AI Technical Summary

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

Images