{"title":"基于加权分量和全局特征的自适应面部表情识别","authors":"Rui Li, Min Hu, Xiaohua Wang, Liangfeng Xu, Zhong Huang, Xing Chen","doi":"10.1109/ICDH.2012.53","DOIUrl":null,"url":null,"abstract":"An adaptive facial expression recognition method based on component and global features is presented in this paper. The facial component features are highlighted for purpose of improving facial expression percent correct rate. Firstly, eyebrows, eyes, nose and mouth are divided from a facial expression image and then the component features would be gotten from these organ images which are processed by Gabor wavelets. The weighted adaptive algorithm would be used to calculate the component feature weights, the weighted component features fuse with the global feature to get a feature fusion matrix. Finally, Weighted Principal Component Analysis (WPCA) and Fisher Linear Discriminant (FLD) methods are used to reduce dimensions and classify facial expression. Experimental results show that the algorithm proposed in this paper has much more accurate recognition rate compared with the global Gabor wavelets, PCA and FLD integrated algorithm.","PeriodicalId":308799,"journal":{"name":"2012 Fourth International Conference on Digital Home","volume":"172 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Adaptive Facial Expression Recognition Based on a Weighted Component and Global Features\",\"authors\":\"Rui Li, Min Hu, Xiaohua Wang, Liangfeng Xu, Zhong Huang, Xing Chen\",\"doi\":\"10.1109/ICDH.2012.53\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An adaptive facial expression recognition method based on component and global features is presented in this paper. The facial component features are highlighted for purpose of improving facial expression percent correct rate. Firstly, eyebrows, eyes, nose and mouth are divided from a facial expression image and then the component features would be gotten from these organ images which are processed by Gabor wavelets. The weighted adaptive algorithm would be used to calculate the component feature weights, the weighted component features fuse with the global feature to get a feature fusion matrix. Finally, Weighted Principal Component Analysis (WPCA) and Fisher Linear Discriminant (FLD) methods are used to reduce dimensions and classify facial expression. Experimental results show that the algorithm proposed in this paper has much more accurate recognition rate compared with the global Gabor wavelets, PCA and FLD integrated algorithm.\",\"PeriodicalId\":308799,\"journal\":{\"name\":\"2012 Fourth International Conference on Digital Home\",\"volume\":\"172 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Fourth International Conference on Digital Home\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDH.2012.53\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fourth International Conference on Digital Home","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDH.2012.53","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive Facial Expression Recognition Based on a Weighted Component and Global Features
An adaptive facial expression recognition method based on component and global features is presented in this paper. The facial component features are highlighted for purpose of improving facial expression percent correct rate. Firstly, eyebrows, eyes, nose and mouth are divided from a facial expression image and then the component features would be gotten from these organ images which are processed by Gabor wavelets. The weighted adaptive algorithm would be used to calculate the component feature weights, the weighted component features fuse with the global feature to get a feature fusion matrix. Finally, Weighted Principal Component Analysis (WPCA) and Fisher Linear Discriminant (FLD) methods are used to reduce dimensions and classify facial expression. Experimental results show that the algorithm proposed in this paper has much more accurate recognition rate compared with the global Gabor wavelets, PCA and FLD integrated algorithm.