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Treffer: Craniofacial Reconstruction Method Based on Region Fusion Strategy.

Title:
Craniofacial Reconstruction Method Based on Region Fusion Strategy.
Authors:
Wen Y; College of Information Science and Technology, Northwest University, Xi'an, China., Mingquan Z; College of Information Science and Technology, Northwest University, Xi'an, China., Pengyue L; College of Information Science and Technology, Northwest University, Xi'an, China., Guohua G; College of Information Science and Technology, Northwest University, Xi'an, China., Xiaoning L; College of Information Science and Technology, Northwest University, Xi'an, China., Kang L; College of Information Science and Technology, Northwest University, Xi'an, China.
Source:
BioMed research international [Biomed Res Int] 2020 Dec 04; Vol. 2020, pp. 8835179. Date of Electronic Publication: 2020 Dec 04 (Print Publication: 2020).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: United States NLM ID: 101600173 Publication Model: eCollection Cited Medium: Internet ISSN: 2314-6141 (Electronic) NLM ISO Abbreviation: Biomed Res Int Subsets: MEDLINE
Imprint Name(s):
Publication: 2024- : [Hoboken, NJ] : Wiley
Original Publication: New York, NY : Hindawi Pub. Co.
MeSH Terms:
Face*/anatomy & histology , Face*/diagnostic imaging , Skull*/anatomy & histology , Skull*/diagnostic imaging, Forensic Anthropology/*methods , Imaging, Three-Dimensional/*methods, Adult ; Aged ; Algorithms ; Female ; Humans ; Male ; Middle Aged ; Regression Analysis ; Support Vector Machine ; Young Adult
References:
Forensic Sci Int. 2005 Nov 25;154(2-3):149-58. (PMID: 16140487)
Forensic Sci Int. 2016 Feb;259:19-31. (PMID: 26773218)
PLoS One. 2019 Jan 23;14(1):e0210257. (PMID: 30673719)
Forensic Sci Int. 2010 Jul 15;200(1-3):50-9. (PMID: 20418033)
Forensic Sci Int. 2011 Jul 15;210(1-3):228-36. (PMID: 21482053)
Forensic Sci Int. 1996 Nov 11;83(1):51-9. (PMID: 8939013)
Forensic Sci Int. 2016 Sep;266:573.e1-573.e12. (PMID: 27544400)
Forensic Sci Int. 2011 May 20;208(1-3):95-102. (PMID: 21185136)
Entry Date(s):
Date Created: 20210125 Date Completed: 20210517 Latest Revision: 20210517
Update Code:
20250114
PubMed Central ID:
PMC7787737
DOI:
10.1155/2020/8835179
PMID:
33490260
Database:
MEDLINE

Weitere Informationen

Craniofacial reconstruction is to estimate a person's face model from the skull. It can be applied in many fields such as forensic medicine, archaeology, and face animation. Craniofacial reconstruction is based on the relationship between the skull and the face to reconstruct the facial appearance from the skull. However, the craniofacial structure is very complex and the relationship is not the same in different craniofacial regions. To better represent the shape changes of the skull and face and make better use of the correlation between different local regions, a new craniofacial reconstruction method based on region fusion strategy is proposed in this paper. This method has the flexibility of finding the nonlinear relationship between skull and face variables and is easy to solve. Firstly, the skull and face are divided into five corresponding local regions; secondly, the five regions of skull and face are mapped to low-dimensional latent space using Gaussian process latent variable model (GP-LVM), and the nonlinear features between skull and face are extracted; then, least square support vector regression (LSSVR) model is trained in latent space to establish the mapping relationship between skull region and face region; finally, perform regional fusion to achieve overall reconstruction. For the unknown skull, first divide the region, then project it into the latent space of the skull region, then use the trained LSSVR model to reconstruct the face of the corresponding region, and finally perform regional fusion to realize the face reconstruction of the unknown skull. The experimental results show that the method is effective. Compared with other regression methods, our method is optimal. In addition, we add attributes such as age and body mass index (BMI) to the mappings to achieve face reconstruction with different attributes.
(Copyright © 2020 Yang Wen et al.)

The authors declare that they have no conflicts of interest regarding the publication of this paper.