The authors have declared that no competing interests exist.
The application of three-dimensional scan models offers a useful resource for studying craniofacial variation. The complex mathematical analysis for facial point acquisition in three-dimensional models has made many craniofacial assessments laborious.
This study investigates three-dimensional (3D) soft-tissue craniofacial variation, with relation to ethnicity, sex and age variables in British and Irish white Europeans. This utilizes a geometric morphometric approach on a subsampled dataset comprising 292 scans, taken from a Liverpool-York Head Model database. Shape variation and analysis of each variable are tested using 20 anchor anatomical landmarks and 480 sliding semi-landmarks.
Significant ethnicity, sex, and age differences are observed for measurement covering major aspects of the craniofacial shape. The ethnicity shows subtle significant differences compared to sex and age; even though it presents the lowest classification accuracy. The magnitude of dimorphism in sex is revealed in the facial, nasal and crania measurement. Significant shape differences are also seen at each age group, with some distinct dimorphic features present in the age groups.
The patterns of shape variation show that white British individuals have a more rounded head shape, whereas white Irish individuals have a narrower head shape. White British persons also demonstrate higher classification accuracy. Regarding sex patterns, males are relatively larger than females, especially in the mouth and nasal regions. Females presented with higher classification accuracy than males. The differences in the chin, mouth, nose, crania, and forehead emerge from different growth rates between the groups. Classification accuracy is best for children and senior adult age groups.
Morphometrics is the study of shape variation and its covariation with other variables [
A flexible and mathematically rigorous interpolation technique of D’Arcy Thompson’s transformation grids [
Craniofacial measurement traditionally has reliance on simple distances and angles between anatomical landmarks. These give only a limited representation of the surface under study [
Characterizing human craniofacial shape for ethnicity classification, sex dimorphism, and age estimation is of interest to numerous fields, including forensics [
However, there is a limitation of amalgamated data from mixed ages to create a single classification model or a single pair of prototypes [
However, since a big part of biological variability cannot be assessed by using only anatomical landmarks [
The aim of this study was to first extend the computational deformation process by [
For ethics approval, the use of human subjects was approved by the committee in charge of the Liverpool-York Head Model in Alder Hey Craniofacial Unit, Liverpool, UK. Therefore, there is no institutional review board approval required to use the public dataset, asides the user license agreement signed between the two parties. Regarding the use of subjects, we have contacted the head of data access committee of the dataset for more clarifications. He clarified that there is no restriction on the use of any or all the subjects under the CC-BY license and also that all subjects signed forms with consent to publish.
This study used a randomly selected sub-sample of 292 (white British = 234, white Irish = 58) craniofacial images from the Headspace dataset. Only white British and white Irish descent, all of whom are wearing tight-fitting latex caps [
White British | White Irish | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Male | Female | Male | Female | Total | ||||||
Age Group | N | % | N | % | N | % | N | % | N | % |
<13 | 21 | 20.59 | 24 | 18.18 | 1 | 3.7 | 1 | 3.23 | 47 | 16.09 |
13–19 | 13 | 12.74 | 11 | 8.34 | 0 | 0 | 3 | 9.68 | 27 | 9.25 |
20–29 | 26 | 25.49 | 32 | 24.24 | 6 | 22.22 | 7 | 22.58 | 71 | 24.31 |
30–49 | 21 | 20.59 | 33 | 25 | 14 | 51.86 | 12 | 38.71 | 80 | 27.40 |
50> | 21 | 20.59 | 32 | 24.24 | 6 | 22.22 | 8 | 25.80 | 67 | 22.95 |
Total | 102 | 100 | 132 | 100 | 27 | 100 | 31 | 100 | 292 | 100 |
A 3D mesh template was created by manually locating twenty anatomical points on a 3D head and face (
The 20 anchor anatomical landmarks are shown in red. The blue are on the pronasale indicates the point where the semi-landmarks begin the sliding process.
Showing 20 anchor anatomical points (red color) and 480 semi-landmarks (blue color) with 1.5 mm radius: (A) Frontal view. (B) Lateral view.
No | Anchor Landmarks | Notation | Description |
---|---|---|---|
1 | Endocanthion left | enl | Left most medial point of the palpebral fissure, at the inner commissure of the eye. |
2 | Exocanthion left | exl | Left most lateral point of the palpebral fissure, at the outer commissure of the eye. |
3 | Exocanthion right | exr | Right most lateral point of the palpebral fissure, at the outer commissure of the eye. |
4 | Endocanthion right | enr | Right most medial point of the palpebral fissure, at the inner commissure of the eye. |
5 | Metopion | me | Median point, instrumentally determined on the frontal head as the greatest elevation from a cord between nasion and glabella. |
6 | Glabella | g | The most prominent midline point of the forehead between the brow ridges |
7 | Nasion | n | The point in the midline of the nasal radix and nasofrontal |
8 | Pronasale | pr | The most prominent point on the nasal tip |
9 | Alare left | all | Left most lateral point on the nasal ala. |
10 | Alare right | alr | Right most lateral point on the nasal ala. |
11 | Cheilion left | chl | Left outer corners of the mouth where the outer edges of the upper and lower vermilions meet. |
12 | Cheilion right | chr | Right outer corners of the mouth where the outer edges of the upper and lower vermilions meet. |
13 | Labiale superius | ls | Midpoint of the vermilion border of the upper lip. |
14 | Labiale inferius | li | Midpoint of the vermilion border of the lower lip. |
15 | Pogonion | pg | The most prominent midline point of the soft tissue chin pad |
16 | Gnathion | gn | The most anterior inferior midline point on the soft tissue chin contour |
17 | Tragion left | tl | The left notch in the superior margin of each tragus |
18 | Tragion right | tr | The right notch in the superior margin of each tragus |
19 | Opisthocranion | op | Most posterior median point of the occipital bone, instrumentally determined as the greatest chord length from glabella |
20 | Vertex | ve | Most superior point of the head. |
The geometry of curves and surfaces is easier in 2D or 3D but it is less easy to define semi-landmarks for non-planar surfaces in 3D [
According to Bookstein [
The interpolation conditions in
(A) Partial sliding on target mesh–first iteration. (B) Complete and homologous warping on target mesh–sixth iteration. (C) Approximate location of selected distance measurement for six regions redacted from [
The steps in this algorithm can be summarised as follows:
Anatomical fixed points (20) were identified and digitized on the template craniofacial mesh and a prominent point (the pronasale) was identified.
Semi-landmarks (480) were automatically generated and placed along the curves located at a uniform distance along each curve for sliding in step (5).
These semi-landmarks were first randomly placed and then uniformly distributed on the selected reference surface mesh, starting from the selected prominent point.
The reference facial model was warped to each target mesh configuration using a TPS transformation, and the surface semi-landmark was projected from the reference facial mesh to the target facial mesh.
The surface and curve semi-landmarks were then slid together in the direction that minimized the bending energy between each target configuration and the reference object. This was done iteratively in six complete cycles, in order to ensure convergence and optimum smoothness. This gave a homologous representation of the reference mesh.
A Generalized Procrustes analysis (GPA) of the landmark data was performed and an error assessment was computed using a Procrustes ANOVA in MorphoJ.
Principal Components Analysis (PCA) is used for dimensionality reduction. The total principal components (PCs) computed during the reduction process is 300PCs. Among these, only 180PCs which have been observed to have the highest ranking eigenvectors are selected for further analyses. This is based on the eigenvalues from random data of the principal components (see
Shape differences among the groups are studied using the aligned coordinates to perform a PCA to describe major trends in shape; between white British and white Irish, between males and females, and among the age classes. The PCs obtained from these variables are known as relative warps [
Due to the visual interpretation of lollipop graph, we further employed the method of Euclidean Distance Matrix Analysis (EDMA) [
The process of landmark coordinate extraction is always associated with some degree of measurement error. This can be as a result of non-coplanarity of landmarks, inconsistent of specimens relative to the plane of digitization, or difficulty in pinpointing the landmark locus [
Analyses for discrimination and allometric patterns are conducted on data averaged for ethnicity, sex, and age. Canonical variate analysis (CVA) and discriminant analysis (DA) are used to test group differences, to plot their differences, and to predict group classification. Using MorphoJ, CVA was performed to test group differences. Furthermore, using PAST, DA was performed by computing cross-validated classification tables to find a set of axes that grants the greatest ability possible to discriminate between two or more groups [
Using Procrustes distance with 10,000 permutations, we assess the statistical significance of the pairwise difference in mean shape; this comes along with Mahalanobis distance but not considered in this study. Because there may be an interaction between the size and shape in craniofacial morphology due to changes in shape associated with size differences [
A full MANOVA with ethnicity as groups, the size as covariate and the ethnicity-by-size interaction term included was performed. We further applied the same on sex and age using the size as covariate to test their interaction. The allometric trajectories are parallel if there is no significance [
After the step-by-step methods in facial surface deformation of semi-landmarks in Viewbox 4.0, the analysis, visualisation and classification of the experiment are performed using MorphoJ 1.06d [
From the scatter plot of PC1 and PC2 scores (
Centre: Showing the first two principal components of the shape variation. The ellipses represent 95% confidence intervals. Sides: Craniofacial deformation representing variation along first two PCs (Upper: white British males, Lower: white British females, Right: white Irish males, Left: white Irish females).
The results for shape are reported in
Effect | Var (%) | SS | MS | DF | F | P |
---|---|---|---|---|---|---|
Ethnicity | 4.71 | 0.1195676 | 0.002255993 | 53 | 15.75 | < .0001 |
Sex | 1.30 | 0.03300793 | 0.000622791 | 53 | 4.35 | < .0001 |
Age | 8.04 | 0.20405101 | 0.000962505 | 212 | 6.72 | < .0001 |
Individual | 85.51 | 2.17086953 | 0.000143216 | 15158 | 3.05 | < .0001 |
Digitizing Error | 0.45 | 0.01135599 | 0.000053566 | 212 | ||
Total | 100 | 2.53885206 | 0.004038071 | 15688 | ||
Ethnicity | 3.07 | 0.0525719 | 3.66866E-05 | 1433 | 10.2 | < .0001 |
Sex | 1.75 | 0.02997836 | 0.00002092 | 1433 | 5.81 | < .0001 |
Age | 8.72 | 0.1492267 | 0.000026034 | 5732 | 7.24 | < .0001 |
Individual | 86.13 | 1.47466996 | 3.5982E-06 | 409838 | 2.48 | < .0001 |
Digitizing Error | 0.33 | 0.00564375 | 9.846E-07 | 5732 | ||
Total | 100 | 1.71209067 | 8.82234E-05 | 424168 | ||
Ethnicity | 3.12 | 0.05389094 | 3.60957E-05 | 1493 | 10.37 | < .0001 |
Sex | 1.72 | 0.02974129 | 1.99205E-05 | 1493 | 5.72 | < .0001 |
Age | 8.69 | 0.15002289 | 0.000025121 | 5972 | 7.22 | < .0001 |
Individual | 86.13 | 1.48630015 | 3.4808E-06 | 426998 | 2.51 | < .0001 |
Digitizing Error | 0.33 | 0.00575018 | 9.629E-07 | 5972 | ||
Total | 100 | 1.72570545 | 8.55809E-05 | 441928 |
SS: sum of squares; MS: mean square; DF: degrees of freedom; F: F-value; P: P-value
Discrimination among groups is analysed independently on the averaged ethnicity, sex, and age; both CVA (plots not shown) and DA indicate that each studied taxon is clearly distinct from one another when pooled within-group variation. Procrustes distance among ethnicity (white British vs. white Irish) is 0.0324, p < 0.0001; Procrustes distance among sex (male vs. female) is 0.02, p < 0.0001. The Procrustes distance among age is shown in
Procrustes/P-values | 12 (years) Below | 13–19 | 20–29 | 30–49 | 50 (years) Above |
---|---|---|---|---|---|
0.0003 | < .0001 | < .0001 | < .0001 | ||
0.0326 | 0.0070 | < .0001 | < .0001 | ||
0.0522 | 0.0280 | 0.0240 | 0.0123 | ||
0.0595 | 0.0376 | 0.0166 | 0.0076 | ||
0.0637 | 0.0400 | 0.0206 | 0.0189 |
From DA in
A. Between ethnicity (white British and white Irish). B. Between sex (Male and Female). C. Scatter plots of canonical variate analysis among the age classes: children (below 13 years), teenagers (13–19 years), young adults (20–29 years), adult (30–49 years), and senior adults (50 years and above); at 95% confidence ellipse.
Ethnicity | White British | White Irish | Age | < 13 | 13–19 | 20–29 | 30–49 | > 50 |
---|---|---|---|---|---|---|---|---|
White British | 99.14 | 0.85 | < 13 | 100 | 0 | 0 | 0 | 0 |
White Irish | 7.46 | 92.54 | 13–19 | 0 | 96.29 | 0 | 0 | 3.7 |
20–29 | 0 | 0 | 98.59 | 1.41 | 0 | |||
30–49 | 0 | 0 | 1.16 | 97.67 | 1.16 | |||
male | 99.25 | 0.75 | 50 > | 0 | 0 | 0 | 0 | 100 |
female | 0 | 100 |
All specimens are scaled to unit centroid size (CS). Rotation and translation parameters are estimated to minimize the sum of squared distances between each soft-tissue craniofacial landmark and those of an iteratively computed mean configuration. Allometry is tested for symmetric components of the averaged groups. In detail, for ethnicity, sex, and age; significant allometric patterns of symmetric variation are detected, with 9.35%, 9.95%, and 2.87%, respectively, all with p < 0.0001. The results of the linear regression of centroid size showed statistically significant patterns (
A. Between white British and white Irish. B. Between Males and Females. C. Among the Age classes: children (below 13 years), teenagers (13–19 years), young adults (20–29 years), adults (30–49 years), and senior adults (50 years and above).
Furthermore, the total variation of each dependent variable is partitioned by the regression model into a component of variations. These components are computed for each variable separately. The residual and predicted components are expressed as a percentage of the total variation, which is intuitively useful to quantify the relative importance of allometry for the shape variation in each sub-divided dataset [
Var | %Predicted | P-value |
---|---|---|
WB | 9.47 | < .0001 |
WI | 7.35 | < .0001 |
Male | 13.34 | < .0001 |
Female | 6.15 | < .0001 |
Below 13 | 5.91 | 0.0025 |
13–19 | 7.04 | 0.0442 |
20–29 | 2.12 | 0.1121 |
30–49 | 3.25 | 0.0038 |
50 Above | 9.31 | < .0001 |
10,000 permutations test
CS and resulting shape variables are used in subsequent multivariate analyses. Using the PCA scores, multivariate analysis (MANOVA) is performed to test the significant effect of ethnicity and size on the shape of sex and age. A MANOVA is applied to the symmetric component of the ethnicity to test for differences in significant allometric trajectories of ethnicity, sex, and age with regard to two features: slopes (ethnicity × CS effect, sex x CS effect, and age x CS effect) of the regression lines and their intercepts [
The MANOVA results are presented in
Effect | Wiki | DF num | DF den | F | P |
---|---|---|---|---|---|
Ethnicity x CS | 0.4159 | 101 | 199 | 2.767 | <0.000 |
Ethnicity | 0.4172 | 100 | 200 | 2.794 | <0.000 |
Sex x CS | 0.2512 | 101 | 199 | 5.872 | <0.000 |
Sex | 0.2514 | 100 | 200 | 5.955 | <0.000 |
Age X CS | 0.01354 | 404 | 786.5 | 3.764 | <0.000 |
Age | 0.01363 | 400 | 790.4 | 3.811 | <0.000 |
In this study, soft-tissue craniofacial variability is investigated for two ethnicities of a subset of white Europeans. In this study, 292 soft-tissue craniofacial landmarks are analysed morphometrically and classified. The primary focus is on the analyses of shape symmetry and the allometric relationships between each ethnic group, but the differences between sex and age groups are also analysed. Using a Procrustes ANOVA [
In order to investigate the overall variation for the entire selected sample, Principal components analysis is performed on all specimens. These analyses are carried out at different levels and the symmetric were analysed. For all computed PCs, PC1 explained almost half of the total variation, which indicates that shape variation is concentrated in a single dimension of the shape space [
Morphological differences between the average of each group (ethnicity, sex, and age) and average estimated 3D faces using PCA.
Landmark distance | Male | Female | |
---|---|---|---|
Cranial | me-op | 2.30 | 2.28 |
Face | me-n | 1.74 | 1.73 |
me-gn | 2.24 | 2.21 | |
tl-tr | 2.19 | 2.18 | |
Eye | enl-enr | 1.93 | 1.92 |
exl-exr | 1.57 | 1.56 | |
Nose | n-pr | 1.68 | 1.64 |
all-alr | 1.39 | 1.38 | |
Mouth | chl-chr | 1.67 | 1.63 |
ls-li | 1.19 | 1.15 | |
Chin | li-gn | 1.46 | 1.45 |
tl-gn | 2.19 | 2.16 | |
Children | Teenagers | Young adults | Adults | Senior adults | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Distance | M | F | M | F | M | F | M | F | M | F | |
Cranial | me-op | 2.29 | 2.27 | 2.30 | 2.29 | 2.30 | 2.29 | 2.30 | 2.29 | 3.22 | 3.20 |
Face | me-n | 1.77 | 1.69 | 1.72 | 1.74 | 1.73 | 1.74 | 1.73 | 1.75 | 1.73 | 1.70 |
me-gn | 2.26 | 2.18 | 2.23 | 2.25 | 2.24 | 2.23 | 2.24 | 2.24 | 3.24 | 3.19 | |
tl-tr | 2.20 | 2.16 | 2.20 | 2.20 | 2.19 | 2.18 | 2.20 | 2.19 | 2.19 | 2.17 | |
Eye | enl-enr | 1.94 | 1.90 | 1.94 | 1.93 | 1.93 | 1.93 | 1.93 | 1.94 | 1.93 | 1.90 |
exl-exr | 1.57 | 1.55 | 1.59 | 1.59 | 1.57 | 1.57 | 1.57 | 1.58 | 1.57 | 1.55 | |
Nose | n-pr | 1.69 | 1.58 | 1.69 | 1.69 | 1.68 | 1.66 | 1.68 | 1.68 | 1.69 | 1.60 |
all-alr | 1.39 | 1.33 | 1.41 | 1.44 | 1.39 | 1.39 | 1.39 | 1.38 | 1.39 | 1.37 | |
Mouth | chl-chr | 1.68 | 1.59 | 1.69 | 1.69 | 1.68 | 1.65 | 1.67 | 1.66 | 1.68 | 1.61 |
ls-li | 1.24 | 1.16 | 1.15 | 1.13 | 1.15 | 1.13 | 1.19 | 1.20 | 1.19 | 1.12 | |
Chin/Jaw | li-gn | 1.38 | 1.38 | 1.44 | 1.42 | 1.48 | 1.44 | 1.48 | 1.45 | 1.48 | 1.49 |
tl-gn | 2.11 | 2.09 | 2.16 | 2.14 | 2.21 | 2.17 | 2.21 | 2.17 | 2.22 | 2.17 | |
Influence on shape is looked into under the three effects. Regarding the ethnicity, white British shows a narrow cranium, whereas white Irish shows a round cranium; and there is more protrusion in the British frontal face than in Irish. Though, no distance measurement was taken on ethnicity group. But regarding the sex influence on shape, a clear effect is identified in the analysis. Based on the centroid size and distances measured of the cranium and face in sex, males show a relatively larger size and sexual dimorphic in mouth width (chelion left-chelion right), mouth height (labiale superius-labiale inferius), nasal height (nasion-pronasale), and nasal bridge length (alare left-alare right), intercanthal width (endocanthion—endocanthion), biocular width (exocanthion-exocanthion), chin length (labiale inferius-gnathion), jaw height (tragion—gnathion), cranial width (metopion-Opisthocranion). It is demonstrated that nasal region increases anteriorly and posteriorly in males than in females. These results indicate that most soft-tissue features of the human head and face show strong evidence of sexual dimorphism which is in alignment with most previously published studies of craniofacial sex differences [
Regarding the age influence on shape, there is an identification of a clear effect in the analysis. The results demonstrate a slightly increase in size among the age classes. More so, the statistically significant difference among age groups is found when the entire cranium size is compared per age group and in the distances measured. The craniofacial features possess consistently larger intra-class variance due to, among others, the cranium changes relative to the increase in body size [
In the children group, males show a slightly larger cranial width. No difference in forehead height but males show longer facial height and wider facial width. The intercanthal width and biocular width in males are slightly longer than those of females. There is no difference in the nasal bridge but the nasal height in males is longer than females. Both mouth width and mouth height are longer in males than in females. There is no difference in the chin length but the jaw length is longer in males.
In the teenagers group, unlike in the children, there is no difference in the cranial width. But facial width, facial height, and forehead length are larger in males. There is no difference in intercanthal width but females biocular width is slightly wider. The nasal bridge length is longer in males than in females but the nasal height is longer in females than in males. Both mouth width and mouth height are larger in females than in males and both the jaw length and chin height are longer in males than in females.
In the young adults group, cranial width is longer in males. Facial width, facial height, and forehead length are larger in males than in females. The intercanthal width and biocular width are slightly wider in males than in females. The nasal bridge length and nasal height are longer in males than in females. Both mouth width and mouth height are larger in males than in females; both jaw length and chin length are longer in males than in females.
In the adults group, like in young adults group, cranial width is longer in males. Facial width, facial height, and forehead length are larger in males than in females. The intercanthal width and biocular width are slightly wider in males than in females. The nasal bridge length and nasal height are longer in males than in females. Both mouth width and mouth height are larger in males than in females; both jaw length and chin length are longer in males than in females.
In the senior adults group, the variations follow that same pattern as in adults group. Except in the mouth width where no difference is observed and also females show a longer jaw length than males. These correspond with previous studies in [
Generally, cranial width is longer in adults; facial width is longer in senior adults; facial height is longer in young adults and senior adults, and the forehead is longer in young adults. The intercanthal width is longer and equal in young adults, adults and senior adults; whereas biocular width is longer only in senior adults. Nasal bridge and nasal height are both longer in senior adults. Mouth width is longer in senior adults and mouth height is longer in young adults. Lastly, both jaw length and chin height are longer in senior adults.
It is examined in this study that allometry has a heterogeneous effect on the soft-tissue craniofacial morphology and that variation occurs in specific regions due to changes in the pattern of growth and development [
Centroid size is not log-transformed, as the transformation makes no appreciable difference in the results (not shown). Regressions of shape onto size of each group are performed at a time and are statistically significant except in young adults (p = 0.1121). This is an indication of negligible allometry. Further statistical analysis is performed to be more confident in the results. In this study, we run MANOVA, which is a simple way to test the effect of size on shape when various groups are compared. Subsequently, the characteristics (i.e. slope and intercept) of the allometric trajectories of each group are tested using MANOVA, which explains significant portions of the overall variation. Regarding the allometric trajectories of ethnicity, the interaction term (test for slopes) is statistically significant (p < 0.000). When the size effect is removed (test for intercepts) and the MANOVA is repeated, the result is still statistically significant (p < 0.000). This suggests that the effect of size on shape is strong, and not similar in the two ethnicities. A significant test for intercepts means that there is support for ethnicity differences using the available samples when the effect of size on shape variation is held constant.
Regarding the allometric trajectories of sex, the interaction term is statistically significant (p < 0.000). When the size effect is removed and the MANOVA is repeated, the result is still statistically significant (p < 0.000). This suggests that the effect of size on shape is strong and not similar in the sex group. For the allometric trajectories of age class, the interaction term is statistically significant (p < 0.000). When the size effect is removed and the MANOVA is repeated, the result is still statistically significant (p < 0.000). This suggests that the effect of size on shape is strong and not similar in the age group. As it is expected since age class has larger phenotypic variations, the allometric trajectories are largely aligned with the vector of mean shape differences.
While the MANOVA and contrast tests detect significant shape differences in each group, DA is further employed to classify each group with moderate cross-validation rates. For ethnicity, the results show that allometric variation is negligible with respect to the ethnicity differentiation; indeed, the sample of white British are correctly classified with 99.14% and white Irish with 92.54%. For sex, 99.25% of males are correctly classified while 100% of females are correctly classified, which shows negligibility in allometric variation with respect to sex. Subsequently, the age group data followed no trend wherein the highest correct classification rates are found in children (100%) and senior adults (100%), followed by young adult (98.59%), then adults (97.67%), and finally teenagers (96.29%). The children and the senior adults age groups are found to exhibit the most variance of the five examined age groups.
The discrimination we observed in the children and adult groups is as a result of the variation in the chin, nose, forehead and crania between the age groups because of biological reasons and activity patterns. Tome et al. [
The study led to the following conclusions relating to soft-tissue craniofacial shape and size variation with ethnicity, sex, and age. Craniofacial size, expressed as centroid size, is less affected by ethnicity. But has a great impact on sex and age. Both craniofacial shape and size are significantly sexually dimorphic, which results in statistically significant differences between males and females; though not considered in different age groups. Attention should, therefore, be given to over-classification problems when DA is applied on the craniofacial shape, when captured by multiple landmarks. However, due to the uncertainty of biological reality reflection, the assigned sliding semi-landmarks may not adequately reflect the shape of the entire head and face under study. This may also have a negative impact on biological variability within the sample related to ethnicity, sex, or age. Furthermore, we face some discrepancy challenges in the dataset used. This is because the dataset comes from various ethnicities and countries with white British having the highest number of sample, followed by white Irish. Other ethnicities are insignificant for analysis consideration due to their infinitesimal sample size. More so, no subject is found in the white Irish males of teenagers (13–19 years) age group. These discrepancies consequently have effect on the interpretation of our results, especially the principal component analysis. While further study is recommended for clarification on the aforementioned issues, this study, nonetheless, combines pragmatic solutions to configure an optimized pipeline for high-throughput multi-point craniofacial signature in 3D to the investigation of ethnicity, sex, and age related variation in craniofacial morphology. Thus, this study is limited to white Europeans descents (British and Irish), therefore the generalizability of these results to other populations cannot be assumed.
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We acknowledge Department of Computer Science, University of York, UK (Liverpool -York Head Model) for prompt agreement to use their dataset, and the Computer Laboratory of the Faculty of Computer Science & Information Technology, Universiti Putra Malaysia.