计算机工程2025，Vol.51Issue(11)：54-62,9.DOI:10.19678/j.issn.1000-3428.0069761
EmoRepLKNet:一种基于UniRepLKNet的面部情绪识别神经网络
EmoRepLKNet:Facial Emotion Recognition Neural Network Based on UniRepLKNet
摘要
Abstract
This study presents a facial emotion recognition network based on UniRepLKNet to address the difficulty in effectively capturing feature information and preventing key facial information from occupying a more prominent position in the facial emotion recognition process.Moreover,to extract facial emotional features more accurately,the study designs a masked polarized self-attention module that combines U-Net and a polarized self-attention mechanism.This module can deeply mine the dependency between channels and spaces.It can also strengthen the influence of local key information of the face on emotion recognition through a multi-scale feature fusion strategy.The study optimizes UniRepLKNet,a universal large kernel Convolutional Neural Network(CNN),and proposes the EmoRepLKNet neural network structure.In EmoRepLKNet,the mask-polarized self-attention module enables the network to extract key information for facial emotion recognition.Combined with the wide receptive field of large kernel CNN,facial emotions can be recognized effectively.Experimental results show that on the facial emotion recognition dataset FER2013,EmoRepLKNet achieves an accuracy of 76.20％,outperforming existing comparison models and significantly improving facial emotion recognition accuracy compared to that of UniRepLKNet.Additionally,on the single-label portion of the RAF-DB dataset,the proposed method achieves an accuracy of 89.67％.关键词
情绪识别/深度学习/大核卷积神经网络/注意力机制/FER2013数据集/RAF-DB数据集Key words
emotion recognition/deep learning/large kernel Convolutional Neural Network(CNN)/attention mechanism/FER2013 dataset/RAF-DB dataset分类
信息技术与安全科学引用本文复制引用
肖志鹏,何书峰,田春岐..EmoRepLKNet:一种基于UniRepLKNet的面部情绪识别神经网络[J].计算机工程,2025,51(11):54-62,9.基金项目
物联网技术应用交通运输行业研发中心(杭州)开放基金(2023-04). （杭州）
