{"title":"多线性AAM在照片间的表达传递","authors":"Ives Macêdo, E. V. Brazil, L. Velho","doi":"10.1109/SIBGRAPI.2006.18","DOIUrl":null,"url":null,"abstract":"Expression transfer is a method for mapping a photographed expression performed by a given subject onto the photograph of another person's face. Building on well succeeded previous works by the vision researchers (facial expression decomposition, active appearance models and multilinear analysis, we propose a novel approach for expression transfer based on color images. We attack this problem with methods developed by the computer vision community for facial expression analysis and recognition. Combining active appearance models and multilinear analysis, it's possible to suitably represent and analyze expressive facial images, while separating both style (subject's identity) and content (expressive flavor) from the captured performance","PeriodicalId":253871,"journal":{"name":"2006 19th Brazilian Symposium on Computer Graphics and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":"{\"title\":\"Expression Transfer between Photographs through Multilinear AAM's\",\"authors\":\"Ives Macêdo, E. V. Brazil, L. Velho\",\"doi\":\"10.1109/SIBGRAPI.2006.18\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Expression transfer is a method for mapping a photographed expression performed by a given subject onto the photograph of another person's face. Building on well succeeded previous works by the vision researchers (facial expression decomposition, active appearance models and multilinear analysis, we propose a novel approach for expression transfer based on color images. We attack this problem with methods developed by the computer vision community for facial expression analysis and recognition. Combining active appearance models and multilinear analysis, it's possible to suitably represent and analyze expressive facial images, while separating both style (subject's identity) and content (expressive flavor) from the captured performance\",\"PeriodicalId\":253871,\"journal\":{\"name\":\"2006 19th Brazilian Symposium on Computer Graphics and Image Processing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"31\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 19th Brazilian Symposium on Computer Graphics and Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIBGRAPI.2006.18\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 19th Brazilian Symposium on Computer Graphics and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIBGRAPI.2006.18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Expression Transfer between Photographs through Multilinear AAM's
Expression transfer is a method for mapping a photographed expression performed by a given subject onto the photograph of another person's face. Building on well succeeded previous works by the vision researchers (facial expression decomposition, active appearance models and multilinear analysis, we propose a novel approach for expression transfer based on color images. We attack this problem with methods developed by the computer vision community for facial expression analysis and recognition. Combining active appearance models and multilinear analysis, it's possible to suitably represent and analyze expressive facial images, while separating both style (subject's identity) and content (expressive flavor) from the captured performance