现代信息科技2025，Vol.9Issue(5)：39-44,50,7.DOI:10.19850/j.cnki.2096-4706.2025.05.007
基于自适应卷积和注意力融合的ResNet人脸表情识别方法
ResNet-based Facial Expression Recognition Method Based on Adaptive Convolution and Attention Fusion
严武军 1叶金霞 1李建昌1
作者信息
- 1. 太原师范学院 计算机科学与技术学院,山西 晋中 030619
- 折叠
摘要
Abstract
In practical applications,the accuracy of facial expression recognition is relatively low because facial images are affected by factors such as lighting,occlusion,and pose.To address this,an expression recognition method that integrates adaptive convolution and the Attention Mechanism is proposed.Based on the ResNet34 network,this method introduces an AKConv module to capture multi-scale features and integrates an ACmix mechanism to improve the classification accuracy.Meanwhile,the traditional cross-entropy loss function is replaced with SlideLoss to address the problem of data imbalance.Experimental results show that this model achieves an accuracy of 75.21%on the FER2013 dataset,verifying its effectiveness and superiority and providing new ideas and methods for the field of facial expression recognition.关键词
ResNet/面部情绪识别/深度学习/注意力机制Key words
ResNet/facial emotion recognition/Deep Learning/Attention Mechanism分类
计算机与自动化引用本文复制引用
严武军,叶金霞,李建昌..基于自适应卷积和注意力融合的ResNet人脸表情识别方法[J].现代信息科技,2025,9(5):39-44,50,7.
