{"title":"基于多通道反卷积的面部表情识别","authors":"G. Krell, R. Niese, B. Michaelis","doi":"10.1109/ICAPR.2009.95","DOIUrl":null,"url":null,"abstract":"Facial expression recognition is an important task in human computer interaction systems to include emotion processing. In this work we present a Multi-Channel Deconvolution method for post processing of face expression data derived from video sequences. Photogrammetric techniques are applied to deter¬mine real world geometric measures and to build the feature vector. SVM classification is used to classify a limited number of emotions from the feature vector. A Multi-Channel Deconvolution removes ambiguities at the transitions between different classified emotions. This way, typical temporal behavior of facial expression change is considered.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Facial Expression Recognition with Multi-channel Deconvolution\",\"authors\":\"G. Krell, R. Niese, B. Michaelis\",\"doi\":\"10.1109/ICAPR.2009.95\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Facial expression recognition is an important task in human computer interaction systems to include emotion processing. In this work we present a Multi-Channel Deconvolution method for post processing of face expression data derived from video sequences. Photogrammetric techniques are applied to deter¬mine real world geometric measures and to build the feature vector. SVM classification is used to classify a limited number of emotions from the feature vector. A Multi-Channel Deconvolution removes ambiguities at the transitions between different classified emotions. This way, typical temporal behavior of facial expression change is considered.\",\"PeriodicalId\":443926,\"journal\":{\"name\":\"2009 Seventh International Conference on Advances in Pattern Recognition\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-02-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Seventh International Conference on Advances in Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAPR.2009.95\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Seventh International Conference on Advances in Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAPR.2009.95","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Facial Expression Recognition with Multi-channel Deconvolution
Facial expression recognition is an important task in human computer interaction systems to include emotion processing. In this work we present a Multi-Channel Deconvolution method for post processing of face expression data derived from video sequences. Photogrammetric techniques are applied to deter¬mine real world geometric measures and to build the feature vector. SVM classification is used to classify a limited number of emotions from the feature vector. A Multi-Channel Deconvolution removes ambiguities at the transitions between different classified emotions. This way, typical temporal behavior of facial expression change is considered.
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