数据采集与处理2024，Vol.39Issue(6)：1493-1504,12.DOI:10.16337/j.1004-9037.2024.06.017
基于金字塔分割注意力和联合损失的表情识别模型
An Expression Recognition Model Based on Pyramid Split Attention and Joint Loss
摘要
Abstract
How to extract multi-scale features and model semantic dependencies between remote channels remains a challenge for expression recognition networks.This paper proposes a residual network based on pyramid split attention(PSA-ResNet),which replaces the 3×3 convolution in the ResNet50 residual module with PSA to effectively extract multi-scale features and enhance the correlation of cross channel information.In order to reduce the differences between similar expressions and expand the distance between different types of expressions,a joint loss function optimization parameter of Softmax loss and Center loss is introduced during the training process.The proposed model is simulated on two publicly available datasets,Fer2013 and CK+,and achieves accuracies of 74.26% and 98.35%,respectively,further confirming that this method has better recognition results compared to cutting-edge algorithms.关键词
表情识别/金字塔分割注意力/多尺度特征/残差网络Key words
expression recognition/pyramid split attention(PSA)/multi-scale feature/residual network分类
信息技术与安全科学引用本文复制引用
谷瑞,顾家乐,宋翠玲..基于金字塔分割注意力和联合损失的表情识别模型[J].数据采集与处理,2024,39(6):1493-1504,12.基金项目
2023年江苏省高职院校教师专业带头人高端研修项目(2023TDFX010). （2023TDFX010）
