Robust 3D Morphable Model Fitting by Sparse SIFT Flow

	Robust 3D Morphable Model Fitting by Sparse SIFT Flow
	Zhu XY(朱翔昱); Yi D(易东); Lei Z(雷震); Li ZQ(李子青)
	2014
会议名称	International Conference on Pattern Recognition, (ICPR).
会议录名称	International Conference on Pattern Recognition (ICPR)
页码	4044 - 4049
会议日期	2014
会议地点	Stockholm, Sweden
摘要	3D Morphable Model (3DMM) has been widely used in face analysis for many years. The most challenging part of 3DMM is to find the correspondences between 3D points and 2D pixels. Existing methods only use keypoints, edges, specular highlights and image pixels to complete the task, which are not accurate or robust. This paper proposes a new algorithm called Sparse SIFT Flow (SSF) to improve the reconstruction accuracy. We mark a set of salient points to control the shape of facial components and use SSF to find their corr
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/4461
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhu XY,Yi D,Lei Z,et al. Robust 3D Morphable Model Fitting by Sparse SIFT Flow[C],2014:4044 - 4049.