Expression recognition method and system based on local and global attention mechanism

Abstract

The invention discloses an expression recognition method and system based on local and global attention mechanisms. The method comprises the following steps: firstly, constructing a neural network model based on a local and global attention mechanism, wherein the model is composed of a shallow feature extraction module, a spatial domain local and global attention module, a residual network module, a multi-scale feature extraction module, a channel domain local and global attention module, a full connection layer and a classification layer; training the neural network model by using sample images in the facial expression image library; and finally, inputting a to-be-tested face image into the trained neural network model for expression recognition. According to the invention, a multi-scale feature extraction module is used to extract texture features of different scales in a face image, so that loss of discriminative expression features is avoided; local and global attention modules of a spatial domain and a channel domain are used for enhancing features which play a key role in expression recognition and have higher discriminability, and the accuracy and robustness of expression recognition can be effectively improved.

Description

technical field [0001] The invention belongs to the field of image processing and expression recognition, in particular to an expression recognition method and system based on a local and global attention mechanism. Background technique [0002] Facial expressions are the most important way for humans to express their inner emotions, and play a very important role in interpersonal communication. Humans usually understand each other's feelings by recognizing expressions. While humans can recognize facial expressions with almost no effort, reliable recognition of expressions by computers remains a huge challenge. The current main application scenarios of facial expression recognition technology include human-computer interaction, security, robot manufacturing, medical treatment, and communication fields. Research on facial expression recognition can make computers have the ability to understand and express emotions like humans, and can promote the development of human-compute...

AI Technical Summary

Problems solved by technology

[0005] Purpose of the invention: Aiming at the problems of low accuracy and poor robustness in existing facial expression recognition methods, the purpose of the present invention is to provide an expression recognition method based on a local and global attention mechanism, which can be extracted by using a multi-scale feature extraction module Texture features of different scales in face images, so as not to lose discriminative expression features; using spatial domain and channel domain local and global attention modules to strengthen more discriminative features that play a key role in expression recognition can effectively improve expression Accuracy and robustness of recognition

Images