1. 中南大学信息科学与工程学院,湖南,长沙,410083
2. 浙江大学计算机辅助设计与 图形学国家重点实验室,浙江,杭州,310058
3. 华为技术有限公司,广东,深圳,518129
4. 中南大学信息科学与工程学院湖南长沙,410083
5. 浙江大学计算机辅助设计与 图形学国家重点实验室浙江杭州,310058
6. 华为技术有限公司广东深圳,518129
纸质出版:2006
移动端阅览
王磊, 邹北骥, 彭小宁, 等. 一种改进的提取人脸面部特征点的[J]. 电子学报, 2006,34(8):1424-1427.
WANG Lei, ZOU Bei-ji, PENG Xiao-ning, et al. An Improved AAM Fitting Algorithm for Extracting Human Facial Features[J]. Acta Electronica Sinica, 2006, 34(8): 1424-1427.
AAM(Active Appearance Model)是一种用来提取人脸特征点的有效方法
由人脸动态表观建模和拟合算法两部分组成.在多种AAM拟合算法中
反向组合法以快速高效著称.但在遇到外物遮挡时
AAM算法的拟合效果会变差.本文在反向组合法的基础上提出了一种基于分层细化掩模的改进算法.实验结果表明
该算法能较好地去除干扰并保留对拟合有用的信息
具有较强的抗干扰鲁棒性.
Active Appearance Model (AAM) is an efficient method for extracting human facial features.It includes active appearance models and the fitting algorithm.Within all kinds of fitting algorithms
the inverse compositional algorithm is one of the most efficient algorithms.However the efficiency of the fitting algorithm will drop when some other objects occlude any parts of human face.An improved AAM fitting algorithm is presented.It not only keeps the superiority of the original inverse composition algorithm but also enhances the ability of anti-jamming.The experiments show that our algorithm can enhance the robustness of the AAM fitting algorithm and keep the useful information when fitting with occlusion.
0
浏览量
1205
下载量
6
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621