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. The method comprises the following steps: acquiring a facial expression image; and inputting the acquired facial expression image into the facial expression recognition model based on the improved channel attention mechanism, and outputting an expression type. The facial expression recognition model based on the improved channel attention mechanism comprises a plurality of processing units, a full connection layer and a Softmax layer which are arranged in sequence, and each processing unit comprises a convolution layer based on a small-scale convolution kernel, 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, and in particular relates to a method and system for recognizing facial expressions 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 convey 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 complicated and inefficient, so this method is gradually replaced by deep learning-based methods. At present, most facial expression recognition based on deep learning learns facial expression featur...

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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