Estimating age and synthesising growth in children and adolescents using 3D facial prototypes
Graphical abstract
Section snippets
Cross-sectional sample
The cross-sectional sample consisted of 452 boys and 442 girls. Participants were included if they had no history of a growth disorder and if their self-reported ethnicity was Australian, European or North American. The age distribution of the sample is illustrated in Fig. 2.
Longitudinal validation sample
A subset of 24 boys and 26 girls who were imaged at age A (M = 6.92, SD = 3.72, range: 0.97–14.10) were recalled for a second image at age B (M = 12.31, SD = 4.46, range: 4.58–20.15) after an interval of several years (M = 5.40, SD =
Age estimation
The analyses were repeated for different values of σ (30 linearly spaced values between 0.5 years and 18 years). Here we report the best estimation accuracy (when σ = 8.95 for males and σ = 11.36 for females).
The mean absolute error in age estimation for the non-linear prototyping method was 1.19 years (SD = 0.97). This was significantly lower than for the linear prototyping method (M = 1.44, SD = 1.13; paired samples t(893) = 7.76, p = <.001), which in turn was lower than the PC1 method (M = 2.24, SD = 1.72;
Discussion
In this study we developed an approach to age estimation and synthetic growth of children and adolescents using 3D facial prototyping. We have validated both approaches by: (1) comparing predicted age to chronological age and (2) comparing synthetically grown faces to actually grown faces of a subset of the sample (n = 50).
Author contributions
H.M. wrote the first draft of this manuscript, designed, implemented and performed all analysis under the supervision of P.C. This is excepting the template warping which was implemented at KU Leuven. A.P., P.C. and J.C. contributed to the initial study design and data collection and to the manuscript content and structure. All authors contributed to manuscript content and revisions. All authors have read and approved the final manuscript.
Acknowledgements
This work was supported by the Batten Foundation, the Royal Children’s Hospital Foundation, the Jigsaw Foundation, the Victorian Government Operational Infrastructure Program, an NHMRC postgraduate scholarship and a Melbourne Research Scholarship from the University of Melbourne.
We would like to thank the 3D photographers Robert Reitmaier and Lloyd Ellis. We thank the children, their families, and the schools that participated: North Melbourne Primary School, Cambridge Primary School, St Mary's
References (51)
- et al.
Improved facial outcome assessment using a 3D anthropometric mask
Int. J. Oral Maxillofac. Surg.
(2012) - et al.
Combined soft and skeletal tissue modelling of normal and dysmorphic midface postnatal development
J. Cranio-Maxillofac. Surg.
(2016) - et al.
Efficient 3D reconstruction for face recognition
Pattern Recognit.
(2005) - et al.
The 3-dimensional construction of the average 11-year-old child face: a clinical evaluation and application
J. Oral Maxillofac. Surg.
(2006) - et al.
Arcial growth with metallic implants in mandibular growth prediction
Am. J. Orthod. Dentofac. Orthop.
(1975) - et al.
3D shape and 2D surface textures of human faces: the role of “averages” in attractiveness and age
Image Vis. Comput.
(1999) - et al.
Construction and use of facial archetypes in anthropology and syndrome diagnosis
Forensic Sci. Int.
(2006) - et al.
A spatially-dense regression study of facial form and tissue depth: towards an interactive tool for craniofacial reconstruction
Forensic Sci. Int.
(2014) - et al.
Birds have paedomorphic dinosaur skulls
Nature
(2012) - et al.
A statistical method for robust 3D surface reconstruction from sparse data