{"title":"Efficient DenseNet Model with Fusion of Channel and Spatial Attention for Facial Expression Recognition","authors":"Dương Thăng Long","doi":"10.2478/cait-2024-0010","DOIUrl":null,"url":null,"abstract":"\n Facial Expression Recognition (FER) is a fundamental component of human communication with numerous potential applications. Convolutional neural networks, particularly those employing advanced architectures like Densely connected Networks (DenseNets), have demonstrated remarkable success in FER. Additionally, attention mechanisms have been harnessed to enhance feature extraction by focusing on critical image regions. This can induce more efficient models for image classification. This study introduces an efficient DenseNet model that utilizes a fusion of channel and spatial attention for FER, which capitalizes on the respective strengths to enhance feature extraction while also reducing model complexity in terms of parameters. The model is evaluated across five popular datasets: JAFFE, CK+, OuluCASIA, KDEF, and RAF-DB. The results indicate an accuracy of at least 99.94% for four lab-controlled datasets, which surpasses the accuracy of all other compared methods. Furthermore, the model demonstrates an accuracy of 83.18% with training from scratch on the real-world RAF-DB dataset.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":"735 ","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/cait-2024-0010","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Facial Expression Recognition (FER) is a fundamental component of human communication with numerous potential applications. Convolutional neural networks, particularly those employing advanced architectures like Densely connected Networks (DenseNets), have demonstrated remarkable success in FER. Additionally, attention mechanisms have been harnessed to enhance feature extraction by focusing on critical image regions. This can induce more efficient models for image classification. This study introduces an efficient DenseNet model that utilizes a fusion of channel and spatial attention for FER, which capitalizes on the respective strengths to enhance feature extraction while also reducing model complexity in terms of parameters. The model is evaluated across five popular datasets: JAFFE, CK+, OuluCASIA, KDEF, and RAF-DB. The results indicate an accuracy of at least 99.94% for four lab-controlled datasets, which surpasses the accuracy of all other compared methods. Furthermore, the model demonstrates an accuracy of 83.18% with training from scratch on the real-world RAF-DB dataset.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.