A landmark-free approach for automatic, dense and robust correspondence of 3D faces

doi:10.1016/j.patcog.2022.108971

	A landmark-free approach for automatic, dense and robust correspondence of 3D faces
	Fan, Zhenfeng 1,4; Hu, Xiyuan 3; Chen, Chen 2,4; Wang, Xiaolian 2,4; Peng, Silong 2,4
	2023
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
卷号	133 页码:14
摘要	Global dense registration of 3D faces commonly prioritizes correspondences of facial landmarks which are fiducial points for the anatomical structures. However, it is not always easy to pre-annotate the land-marks accurately in raw scans of 3D faces. Contrary to the current state-of-the-art in dense 3D face cor-respondence, we propose a general framework without pre-annotated landmarks, which promotes its ro-bustness and allows the meshes to deform in a uniform manner. The proposed framework includes two stages: first the correspondences are established using a template face; and then we select some well -reconstructed samples to build a prior model and leverage it into the correspondence process of other samples. In both stages, the dense registration is revisited in two perspectives: semantic and topological correspondence. In the latter stage, we further incorporate shape and normal statistics of 3D faces to reg-ularize the correspondence process for more robust results. This provides a feasible way to handle data with noises and occlusions, as well as large deformation caused by facial expressions. Our basic idea is to gradually refine the correspondence of individual points in a way global-to-local. At the same time, we solve the local-to-global deformation based on the refined correspondences. The two processes are alternated, and aided by some confidence checks for each individual points. In the experiments, the pro-posed method is evaluated both qualitatively and quantitatively on three datasets including two publicly available ones: FRGC v2.0 and BU-3DFE datasets, demonstrating its effectiveness.(c) 2022 Elsevier Ltd. All rights reserved.
关键词	3D face Dense correspondence Non -rigid registration
DOI	10.1016/j.patcog.2022.108971
收录类别	SCI
语种	英语
资助项目	National Science Foundation of China[NSFC 62106250] ; China Postdoctoral Science Foundation[2021M703272] ; Liaoning Collaboration Innovation Center
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000863094500008
出版者	ELSEVIER SCI LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19801
专题	中国科学院计算技术研究所期刊论文
通讯作者	Hu, Xiyuan
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 3.Nanjing Univ Sci & Technol, Nanjing, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Fan, Zhenfeng,Hu, Xiyuan,Chen, Chen,et al. A landmark-free approach for automatic, dense and robust correspondence of 3D faces[J]. PATTERN RECOGNITION,2023,133:14.
APA	Fan, Zhenfeng,Hu, Xiyuan,Chen, Chen,Wang, Xiaolian,&Peng, Silong.(2023).A landmark-free approach for automatic, dense and robust correspondence of 3D faces.PATTERN RECOGNITION,133,14.
MLA	Fan, Zhenfeng,et al."A landmark-free approach for automatic, dense and robust correspondence of 3D faces".PATTERN RECOGNITION 133(2023):14.