Facial expression recognition method and system based on improved channel attention mechanism

Abstract

The invention discloses a facial expression recognition method and system based on an improved channel attention mechanism in the technical field of facial expression recognition, comprising: collecting facial expression images; inputting the collected facial expression images into the improved channel-based In the facial expression recognition model of the attention mechanism, the expression type is output. The facial expression recognition model based on the improved channel attention mechanism includes several processing units, a fully connected layer and a Softmax layer arranged in sequence, and each of the processing units includes a volume based on a small-scale convolution kernel. Convolution layers, an improved channel attention mechanism module, and a pooling layer. The accuracy of facial expression recognition is improved, and the facial expression recognition model based on the improved channel attention mechanism has better robustness.

Description

technical field [0001] The invention belongs to the technical field of facial expression recognition, in particular to a facial expression recognition method and system based on an improved channel attention mechanism. Background technique [0002] Facial expression recognition has always been one of the research hotspots in the field of computer vision. Facial expression recognition is an important way to transmit emotional information, and it has a wide range of applications in human-computer interaction, recommendation systems, medical research and other fields. [0003] At present, the research on facial expression recognition is mainly based on two methods: traditional manual feature extraction and deep learning. Traditional manual feature extraction is too complex and inefficient, so this method is gradually replaced by deep learning-based methods. At present, most of the facial expression recognition based on deep learning has achieved good results by learning facia...

AI Technical Summary

Problems solved by technology

However, due to different factors such as different age groups, different genders, and living backgrounds, each person interprets the same expression in different ways, resulting in large differences within the class, which is not conducive to expression recognition

Most of the existing convolutional neural networks cannot extract discriminative features, which is not conducive to the improvement of the accuracy of facial expression recognition algorithms

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