{"title":"眼睛和眉毛的自动分割参数模型","authors":"Z. Hammal, A. Caplier","doi":"10.1109/IAI.2004.1300961","DOIUrl":null,"url":null,"abstract":"The aim of our work is automatic facial expression analysis based on the study of temporal evolution of facial feature boundaries. Previously, we developed a robust and fast algorithm for accurate lip contour segmentation (Eveno, N. et al., IEEE Trans. Circuits and Systems for Video Technology, 2004). Now, we focus on eye and eyebrow boundary extraction. The segmentation of eyes and eyebrows involves three steps: first, an accurate model based on flexible curves is defined for each feature; second, models are initialized on the image to be processed after the detection of characteristic points such as eye corners; third, models are accurately fitted to the facial features of an image according to some information of luminance gradient. The performance of our method is evaluated by a quantitative comparison with a manual ground truth and also by the analysis of expression skeletons based on the results of our facial features segmentation.","PeriodicalId":326040,"journal":{"name":"6th IEEE Southwest Symposium on Image Analysis and Interpretation, 2004.","volume":"27 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Eyes and eyebrows parametric models for automatic segmentation\",\"authors\":\"Z. Hammal, A. Caplier\",\"doi\":\"10.1109/IAI.2004.1300961\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of our work is automatic facial expression analysis based on the study of temporal evolution of facial feature boundaries. Previously, we developed a robust and fast algorithm for accurate lip contour segmentation (Eveno, N. et al., IEEE Trans. Circuits and Systems for Video Technology, 2004). Now, we focus on eye and eyebrow boundary extraction. The segmentation of eyes and eyebrows involves three steps: first, an accurate model based on flexible curves is defined for each feature; second, models are initialized on the image to be processed after the detection of characteristic points such as eye corners; third, models are accurately fitted to the facial features of an image according to some information of luminance gradient. The performance of our method is evaluated by a quantitative comparison with a manual ground truth and also by the analysis of expression skeletons based on the results of our facial features segmentation.\",\"PeriodicalId\":326040,\"journal\":{\"name\":\"6th IEEE Southwest Symposium on Image Analysis and Interpretation, 2004.\",\"volume\":\"27 5\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"6th IEEE Southwest Symposium on Image Analysis and Interpretation, 2004.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAI.2004.1300961\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"6th IEEE Southwest Symposium on Image Analysis and Interpretation, 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAI.2004.1300961","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
摘要
我们的工作目标是基于面部特征边界时间演化的自动面部表情分析。之前,我们开发了一种鲁棒且快速的精确唇轮廓分割算法(Eveno, N. et al., IEEE Trans.)。视频技术电路和系统,2004)。现在,我们专注于眼睛和眉毛边界的提取。眼睛和眉毛的分割分为三个步骤:首先,为每个特征定义基于柔性曲线的精确模型;其次,在检测到眼角等特征点后,在待处理图像上初始化模型;第三,根据亮度梯度的一些信息,将模型精确拟合到图像的面部特征上。我们的方法的性能是通过与人工地面真值的定量比较以及基于我们的面部特征分割结果的表情骨架分析来评估的。
Eyes and eyebrows parametric models for automatic segmentation
The aim of our work is automatic facial expression analysis based on the study of temporal evolution of facial feature boundaries. Previously, we developed a robust and fast algorithm for accurate lip contour segmentation (Eveno, N. et al., IEEE Trans. Circuits and Systems for Video Technology, 2004). Now, we focus on eye and eyebrow boundary extraction. The segmentation of eyes and eyebrows involves three steps: first, an accurate model based on flexible curves is defined for each feature; second, models are initialized on the image to be processed after the detection of characteristic points such as eye corners; third, models are accurately fitted to the facial features of an image according to some information of luminance gradient. The performance of our method is evaluated by a quantitative comparison with a manual ground truth and also by the analysis of expression skeletons based on the results of our facial features segmentation.