| 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
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| Source Publication | PATTERN RECOGNITION
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| ISSN | 0031-3203 |
| 2023 | |
| Volume | 133Pages:14 |
| Corresponding Author | Hu, Xiyuan(huxy@njust.edu.cn) |
| Abstract | 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. |
| Keyword | 3D face Dense correspondence Non -rigid registration |
| DOI | 10.1016/j.patcog.2022.108971 |
| WOS Keyword | RECOGNITION ; REGISTRATION ; MODELS ; POINT |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Science Foundation of China[NSFC 62106250] ; China Postdoctoral Science Foundation[2021M703272] ; Liaoning Collaboration Innovation Center |
| Funding Organization | National Science Foundation of China ; China Postdoctoral Science Foundation ; Liaoning Collaboration Innovation Center |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000863094500008 |
| Publisher | ELSEVIER SCI LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://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 Author | Hu, Xiyuan |
| Affiliation | 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 |
| 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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