{"title":"Facial Expression Recognition with Squeeze-and-Excitation Network","authors":"Peiyuan Guo, Chenglong Song","doi":"10.1109/ICSP54964.2022.9778358","DOIUrl":null,"url":null,"abstract":"In this paper, we aim to propose a facial expression recognition algorithm by utilizing the spatial attention mechanism. We adopt 3 basic CNN models, including AlexNet, VGGNet and ResNet for comparison. Then we add the attention module, i.e., SENet to VGGNet and ResNet for futher feature enhancement. Our results on FER2013 show the effectiveness of our method. Our VGG+SENet achieves 65.0% accuracy and our ResNet+SENet achieves 66.8% accuracy. Both method with attention can get an obvious performance promotion, which validates the effectiveness of attention mechanism.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSP54964.2022.9778358","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, we aim to propose a facial expression recognition algorithm by utilizing the spatial attention mechanism. We adopt 3 basic CNN models, including AlexNet, VGGNet and ResNet for comparison. Then we add the attention module, i.e., SENet to VGGNet and ResNet for futher feature enhancement. Our results on FER2013 show the effectiveness of our method. Our VGG+SENet achieves 65.0% accuracy and our ResNet+SENet achieves 66.8% accuracy. Both method with attention can get an obvious performance promotion, which validates the effectiveness of attention mechanism.