Real-time 3D face modeling based on 3D face imaging.

	Real-time 3D face modeling based on 3D face imaging.
	Zhan, Shu; Chang, Lele; Zhao, Jingjing; Kurihara, Toru; Du, Hao; Tang, Yucheng; Cheng, Jun
刊名	NEUROCOMPUTING
	2017
文献子类	期刊论文
英文摘要	Traditional Iterative Closest Point (ICP) can not properly process the noise, outliers and missing data in face imaging, which would result in low accuracy of face image, face image registration error and much more noise in face image, to solve the above problems, an enhanced sparse ICP to register the 3D point clouds in face imaging is proposed. Sparse Iterative Closest Point (SICP) addressed these problems by formulating the registration optimization, which used sparsity inducing norms, moreover, a fast segmentation algorithm for head area segmentation in depth image was proposed. Based on the proposed fast segmentation algorithm and sparse ICP, a new real time 3D face modeling system was set up, which could generate real time 3D face models with high quality by using a depth camera (such as Kinect) even the background of face imaging was complicated. (C) 2017 Elsevier B.V. All rights reserved.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/11642]
专题	深圳先进技术研究院_集成所
作者单位	NEUROCOMPUTING
推荐引用方式 GB/T 7714	Zhan, Shu,Chang, Lele,Zhao, Jingjing,et al. Real-time 3D face modeling based on 3D face imaging.[J]. NEUROCOMPUTING,2017.
APA	Zhan, Shu.,Chang, Lele.,Zhao, Jingjing.,Kurihara, Toru.,Du, Hao.,...&Cheng, Jun.(2017).Real-time 3D face modeling based on 3D face imaging..NEUROCOMPUTING.
MLA	Zhan, Shu,et al."Real-time 3D face modeling based on 3D face imaging.".NEUROCOMPUTING (2017).