{"title":"抗表情、姿态和遮挡的3D面部标记","authors":"H. Dibeklioğlu, A. A. Salah, L. Akarun","doi":"10.1109/SIU.2009.5136436","DOIUrl":null,"url":null,"abstract":"This paper contrasts two approaches to facial landmarking in 3D. The first approach is statistical in nature, and is based on modeling the shape of each feature with Gaussian mixtures. The advantage of this approach is the uniform treatment of landmarks. The second approach is a hybrid method to find the nose tip, which does not require learning, and is robust under adverse conditions. We demonstrate the accuracy and cross-database performance of these methods on FRGC and Bosphorus databases.","PeriodicalId":219938,"journal":{"name":"2009 IEEE 17th Signal Processing and Communications Applications Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Expression, pose and occlusion resistant 3D facial landmarking\",\"authors\":\"H. Dibeklioğlu, A. A. Salah, L. Akarun\",\"doi\":\"10.1109/SIU.2009.5136436\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper contrasts two approaches to facial landmarking in 3D. The first approach is statistical in nature, and is based on modeling the shape of each feature with Gaussian mixtures. The advantage of this approach is the uniform treatment of landmarks. The second approach is a hybrid method to find the nose tip, which does not require learning, and is robust under adverse conditions. We demonstrate the accuracy and cross-database performance of these methods on FRGC and Bosphorus databases.\",\"PeriodicalId\":219938,\"journal\":{\"name\":\"2009 IEEE 17th Signal Processing and Communications Applications Conference\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE 17th Signal Processing and Communications Applications Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2009.5136436\",\"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 IEEE 17th Signal Processing and Communications Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2009.5136436","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Expression, pose and occlusion resistant 3D facial landmarking
This paper contrasts two approaches to facial landmarking in 3D. The first approach is statistical in nature, and is based on modeling the shape of each feature with Gaussian mixtures. The advantage of this approach is the uniform treatment of landmarks. The second approach is a hybrid method to find the nose tip, which does not require learning, and is robust under adverse conditions. We demonstrate the accuracy and cross-database performance of these methods on FRGC and Bosphorus databases.
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