scholarly journals A computerized facial approximation method for archaic humans based on dense facial soft tissue thickness depths

2021 ◽  
Vol 13 (11) ◽  
Author(s):  
Wuyang Shui ◽  
Yameng Zhang ◽  
Xiujie Wu ◽  
Mingquan Zhou
Keyword(s):  
Soft Tissue ◽  
Modern Human ◽  
Middle Pleistocene ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Skull Morphology ◽  
Morphological Variations ◽  
Soft Tissue Thickness ◽  
Facial Approximation ◽  
Upper Face

Abstract Facial approximation (FA) is a common tool used to recreate the possible facial appearance of a deceased person based on the relationship between soft tissue and the skull. Although this technique has been primarily applied to modern humans in the realm of forensic science and archaeology, only a few studies have attempted to produce FAs for archaic humans. This study presented a computerized FA approach for archaic humans based on the assumption that the facial soft tissue thickness depths (FSTDs) of modern living humans are similar to those of archaic humans. Additionally, we employed geometric morphometrics (GM) to examine the geometric morphological variations between the approximated faces and modern human faces. Our method has been applied to the Jinniushan (JNS) 1 archaic human, which is one of the most important fossils of the Middle Pleistocene, dating back to approximately 260,000 BP. The overall shape of the approximated face has a relatively lower forehead and robust eyebrows; a protruding, wider, and elongated middle and upper face; and a broad and short nose. Results also indicate skull morphology and the distribution of FSTDs influence the approximated face. These experiments demonstrate that the proposed method can approximate a plausible and reproducible face of an archaic human.

scholarly journals Towards the restoration of ancient hominid craniofacial anatomy: Chimpanzee morphology reveals covariation between craniometrics and facial soft tissue thickness

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0245760
Author(s):  
Ryan M. Campbell ◽  
Gabriel Vinas ◽  
Maciej Henneberg
Keyword(s):  
Soft Tissue ◽  
Regression Models ◽  
Pan Paniscus ◽  
Modern Human ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness ◽  
Early Hominids ◽  
Values Assessment ◽  
The Face

In modern humans, facial soft tissue thicknesses have been shown to covary with craniometric dimensions. However, to date it has not been confirmed whether these relationships are shared with non-human apes. In this study, we analyze these relationships in chimpanzees (Pan troglodytes) with the aim of producing regression models for approximating facial soft tissue thicknesses in Plio-Pleistocene hominids. Using CT scans of 19 subjects, 637 soft tissue, and 349 craniometric measurements, statistically significant multiple regression models were established for 26 points on the face and head. Examination of regression model validity resulted in minimal differences between observed and predicted soft tissue thickness values. Assessment of interspecies compatibility using a bonobo (Pan paniscus) and modern human subject resulted in minimal differences for the bonobo but large differences for the modern human. These results clearly show that (1) soft tissue thicknesses covary with craniometric dimensions in P. troglodytes, (2) confirms that such covariation is uniformly present in both extant Homo and Pan species, and (3) suggests that chimp-derived regression models have interspecies compatibility with hominids who have similar craniometric dimensions to P. troglodytes. As the craniometric dimensions of early hominids, such as South African australopithecines, are more similar to P. troglodytes than those of H. sapiens, chimpanzee-derived regression models may be used for approximating their craniofacial anatomy. It is hoped that the results of the present study and the reference dataset for facial soft tissue thicknesses of chimpanzees it provides will encourage further research into this topic.

scholarly journals Towards the restoration of ancient hominid craniofacial anatomy: Chimpanzee morphology reveals covariation between craniometrics and facial soft tissue thickness

2021 ◽  
Author(s):  
Ryan Miles Campbell ◽  
Gabriel Vinas ◽  
Maciej Henneberg
Keyword(s):  
Soft Tissue ◽  
Regression Models ◽  
Pan Paniscus ◽  
Homo Sapiens ◽  
Modern Human ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness ◽  
Values Assessment ◽  
The Face

In modern humans, facial soft tissue thicknesses have been shown to covary with craniometric dimensions. However, to date it has not been confirmed whether these relationships are shared with non-human apes. In this study, we analyze these relationships in chimpanzees ( Pan troglodytes ) with the aim of producing regression models for approximating facial soft tissue thicknesses in Plio-Pleistocene hominid individuals. Using CT scans of 19 subjects, 637 soft tissue, and 349 craniometric measurements, statistically significant multiple regression models were established for 26 points on the face and head. Examination of regression model validity resulted in minimal differences between observed and predicted soft tissue thickness values. Assessment of interspecies compatibility using a bonobo ( Pan paniscus ) and modern human  (Homo sapiens ) subject resulted in minimal differences for the bonobo but large differences for the modern human. These results clearly show that (1) soft tissue thicknesses covary with craniometric dimensions in  P. troglodytes , (2) confirms that such covariation is uniformly present in both extant  Homo  and  Pan  species, and (3) suggests that chimp-derived regression models have interspecies compatibility with hominids who have similar craniometric dimensions to  P. troglodytes . As the craniometric dimensions of early hominids, such as South African australopithecines, are more similar to  P. troglodytes  than those of  H. sapiens , chimpanzee-derived regression models may be used for approximating their craniofacial anatomy. It is hoped that the results of the present study and the reference dataset for facial soft tissue thicknesses of chimpanzees it provides will encourage further research into this topic.

A dense approach for computation of facial soft tissue thickness data

2021 ◽  
pp. 200460
Author(s):  
Diana Toneva ◽  
Silviya Nikolova ◽  
Stanislav Harizanov ◽  
Dora Zlatareva ◽  
Vassil Hadjidekov
Keyword(s):  
Soft Tissue ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness

Midsagittal facial soft tissue thickness norms in an adult Mediterranean population

2019 ◽  
Vol 294 ◽  
pp. 217.e1-217.e7 ◽  
Author(s):  
Fouad Ayoub ◽  
Maria Saadeh ◽  
Georges Rouhana ◽  
Ramzi Haddad
Keyword(s):  
Soft Tissue ◽  
Mediterranean Population ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness

Testing regression and mean model approaches to facial soft-tissue thickness estimation

2020 ◽  
pp. 002580242097701
Author(s):  
Tobias MR Houlton ◽  
Nicolene Jooste ◽  
Maryna Steyn
Keyword(s):  
Soft Tissue ◽  
Regression Model ◽  
South African ◽  
Goodness Of Fit ◽  
Regression Equations ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness ◽  
Subject Specific ◽  
Model Approach

Average facial soft-tissue thickness (FSTT) databanks are continuously developed and applied within craniofacial identification. This study considered and tested a subject-specific regression model alternative for estimating the FSTT values for oral midline landmarks using skeletal projection measurements. Measurements were taken from cone-beam computed tomography scans of 100 South African individuals (60 male, 40 female; Mage = 35 years). Regression equations incorporating sex categories were generated. This significantly improved the goodness-of-fit ( r2-value). Validation tests compared the constructed regression models with mean FSTT data collected from this study, existing South African FSTT data, a universal total weighted mean approach with pooled demographic data and collection techniques and a regression model approach that uses bizygomatic width and maximum cranial breadth dimensions. The generated regression equations demonstrated individualised results, presenting a total mean inaccuracy (TMI) of 1.53 mm using dental projection measurements and 1.55 mm using cemento-enamel junction projection measurements. These slightly outperformed most tested mean models (TMI ranged from 1.42 to 4.43 mm), and substantially outperformed the pre-existing regression model approach (TMI = 5.12 mm). The newly devised regressions offer a subject-specific solution to FSTT estimation within a South African population. A continued development in sample size and validation testing may help substantiate its application within craniofacial identification.

scholarly journals Variation of Facial Soft Tissue Thickness in Nepalese Adult Orthodontic Subjects

2018 ◽  
Vol 8 (2) ◽  
pp. 22-28
Author(s):  
Ravi Kumar Mahto ◽  
Dashrath Kafle ◽  
Pankaj Kumar Singh ◽  
Sonika Khanal ◽  
Siddhartha Khanal
Keyword(s):  
Soft Tissue ◽  
Measurement Data ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Male And Female ◽  
Skeletal Class ◽  
Skeletal Malocclusion ◽  
Soft Tissue Thickness ◽  
Significant Difference ◽  
Female Subjects

Introduction: Variations in facial soft tissue thickness have been established previously by studies conducted in different population. Hence, it is essential to obtain facial soft tissue thickness measurement data specific to a population and develop individual standards. The objective of this research is to obtain facial soft tissue thickness data of Nepalese adult male and female subjects seeking orthodontic treatment with different sagittal skeletal malocclusion and evaluate variations in facial soft tissue thickness. Materials & Method: Facial soft tissue thicknesses was measured manually on ninety pretreatment lateral cephalogram at eleven points (Glabella, Nasion, Rhinion, Subnasale, Labrale superius, Stomion, Labrale inferius, Labiomentale, Pogonion,Gnathion and Menton). One-way Analysis of variances [one-way ANOVA] followed by Least significant difference (LSD) post hoc test was used to determine difference in facial soft tissue thickness measurements among three sagittal skeletal group for both sexes. In addition, Student’s t-test was used to find difference in facial soft tissue thickness between the male and female subjects in each skeletal Class. Result: Statistically significant differences were found at points Rhinion, Subnasale, Labrale superius, Stomion and Gnathion in males and at Subnasale, Labrale superius, Stomion and Labrale inferius in females while comparing facial soft tissue thickness among three sagittal skeletal classes. Also, it was observed that mean facial soft tissue thickness was greater for males as compared to female subjects with significant differences at Subnasale, Labrale superius, and Labrale inferius in each skeletal Class. Conclusion: Facial soft tissue thickness varies considerably among different population group, sex and sagittal relationship of jaws.

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