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A landmark-free approach for automatic, dense and robust correspondence of 3D faces
Fan, Zhenfeng1,4; Hu, Xiyuan3; Chen, Chen2,4; Wang, Xiaolian2,4; Peng, Silong2,4
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2023
Volume133Pages:14
Corresponding AuthorHu, Xiyuan(huxy@njust.edu.cn)
AbstractGlobal 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.
Keyword3D face Dense correspondence Non -rigid registration
DOI10.1016/j.patcog.2022.108971
WOS KeywordRECOGNITION ; REGISTRATION ; MODELS ; POINT
Indexed BySCI
Language英语
Funding ProjectNational Science Foundation of China[NSFC 62106250] ; China Postdoctoral Science Foundation[2021M703272] ; Liaoning Collaboration Innovation Center
Funding OrganizationNational Science Foundation of China ; China Postdoctoral Science Foundation ; Liaoning Collaboration Innovation Center
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000863094500008
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/50356
Collection智能制造技术与系统研究中心_多维数据分析
Corresponding AuthorHu, Xiyuan
Affiliation1.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
Recommended Citation
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.
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