Expression recognition method for optimizing convolutional neural network based on improved particle swarm optimization algorithm

Abstract

The invention relates to an expression recognition method for optimizing a convolutional neural network based on an improved particle swarm optimization algorithm. The expression recognition method constructs a convolutional neural network suitable for expression recognition, combines the hybrid particle swarm optimization algorithm with a crossover mutation algorithm and a particle swarm optimization algorithm in a genetic algorithm, optimizes the constructed convolutional neural network by using the hybrid particle swarm optimization algorithm, and solves the problems of gradient disappearance and falling into a local optimal solution in the training process of the convolutional neural network, so as to enable the network convergence speed to be increased and higher in the accuracy. Theexpression recognition method comprises the following steps: (1) performing preprocessing of gray normalization and scale normalization on an expression data set; (2) constructing a convolutional neural network suitable for expression recognition; (3) improving a particle swarm optimization algorithm by using a crossover mutation algorithm in the genetic algorithm, (4) optimizing parameters of theconvolutional neural network by using the improved particle swarm optimization algorithm, and (5) training and testing the optimized convolutional neural network by taking a preprocessed expression data set.

Description

Technical field [0001] The invention patent belongs to the field of intelligent image recognition, and more specifically, relates to a facial expression recognition method based on an improved particle swarm algorithm to optimize a convolutional neural network. Background technique [0002] With the development of artificial intelligence technology and big data, machines have begun to have the same learning ability as the human brain and process huge amounts of information and data. Human facial expressions are also a kind of information. Humans can obtain some information from the other's facial expressions to understand each other's emotions in communication, and now the facial expression information can also be obtained by using intelligent image recognition methods. Facial expression recognition can be applied in the fields of human-computer interaction, security, robotics manufacturing, medical treatment, communications, and automobiles. It has strong market demand and broad...

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Problems solved by technology

[0005] The purpose of the patent of the present invention is to overcome the deficiencies of the prior art, and propose a facial expression recognition method based on the improved particle swarm algorithm to optimize the convolutional neural network, and combine the improved particle swarm algorithm with the convolutional neural network to avoid artificial feature extraction The unstable factors brought by it solve the problems that are prone to occur in the convolutional neural network, so that the expression recognition has a certain improvement in the convergence speed, recognition accuracy and stability

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