{"title":"基于注意机制的多特征面部表情识别","authors":"Menghan Xu, Jingying Ji, Cheng Fang","doi":"10.1109/IHMSC55436.2022.00024","DOIUrl":null,"url":null,"abstract":"Expressions are an important way to communicate human emotional information. In order to address the inadequate capablity of a single convolutional neural network to characterize expressions, a multi-feature expression recognition method based on attention mechanism (AM-FER) is proposed. The method first uses the residual network as the base network to extract features, next uses the attention module to locate useful information and suppress the influence of useless features; then divides the output same-level size features into a stage, constructs a 4-layer feature pyramid network and performs expression prediction separately, and at last fuses the predicted values at the decision layer to obtain the final recognition result. The proposed AM-FER method achieves 73.64% recognition accuracy in the Fer2013 dataset, which is a 3.79% improvement over the original ResNet network, verifying the effectiveness of the algorithm; experiments are conducted for each expression category separately, and there is a significant improvement, with the most significant improvement of 17.4% for the recognition of fear expressions.","PeriodicalId":447862,"journal":{"name":"2022 14th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-Feature Facial Expression Recognition Based on Attention Mechanism\",\"authors\":\"Menghan Xu, Jingying Ji, Cheng Fang\",\"doi\":\"10.1109/IHMSC55436.2022.00024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Expressions are an important way to communicate human emotional information. In order to address the inadequate capablity of a single convolutional neural network to characterize expressions, a multi-feature expression recognition method based on attention mechanism (AM-FER) is proposed. The method first uses the residual network as the base network to extract features, next uses the attention module to locate useful information and suppress the influence of useless features; then divides the output same-level size features into a stage, constructs a 4-layer feature pyramid network and performs expression prediction separately, and at last fuses the predicted values at the decision layer to obtain the final recognition result. The proposed AM-FER method achieves 73.64% recognition accuracy in the Fer2013 dataset, which is a 3.79% improvement over the original ResNet network, verifying the effectiveness of the algorithm; experiments are conducted for each expression category separately, and there is a significant improvement, with the most significant improvement of 17.4% for the recognition of fear expressions.\",\"PeriodicalId\":447862,\"journal\":{\"name\":\"2022 14th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 14th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IHMSC55436.2022.00024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 14th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC55436.2022.00024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-Feature Facial Expression Recognition Based on Attention Mechanism
Expressions are an important way to communicate human emotional information. In order to address the inadequate capablity of a single convolutional neural network to characterize expressions, a multi-feature expression recognition method based on attention mechanism (AM-FER) is proposed. The method first uses the residual network as the base network to extract features, next uses the attention module to locate useful information and suppress the influence of useless features; then divides the output same-level size features into a stage, constructs a 4-layer feature pyramid network and performs expression prediction separately, and at last fuses the predicted values at the decision layer to obtain the final recognition result. The proposed AM-FER method achieves 73.64% recognition accuracy in the Fer2013 dataset, which is a 3.79% improvement over the original ResNet network, verifying the effectiveness of the algorithm; experiments are conducted for each expression category separately, and there is a significant improvement, with the most significant improvement of 17.4% for the recognition of fear expressions.