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Modelling of facial growth in Czech children based on longitudinal data: Age progression from 12 to 15 years using 3D surface models
Abstract
Dealing with the increasing number of long-term missing children and juveniles requires more precise and objective age progression techniques for the prediction of their current appearance. Our contribution includes detailed and real facial growth information used for modelling age progression during adolescence. This study was based on an evaluation of the overall 180 three-dimensional (3D) facial scans of Czech children (23 boys, 22 girls), which were longitudinally studied from 12 to 15 years of age and thus revealed the real growth-related changes. The boys underwent more marked changes compared with the girls, especially in the regions of the eyebrow ridges, nose and chin. Using modern geometric morphometric methods, together with their applications, we modelled the ageing and allometric trajectories for both sexes and simulated the age-progressed effects on facial scans. The facial parts that are important for facial recognition (eyes, nose, mouth and chin) all deviated less than 0.75mm, whereas the areas with the largest deviations were situated on the marginal parts of the face. The mean error between the predicted and real facial morphology obtained by modelling the children from 12 to 15 years of age was 1.92mm in girls and 1.86mm in boys. This study is beneficial for forensic artists as it reduces the subjectivity of age progression methods.
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In a previous study [23], we found a strong influence of age on facial soft tissue morphology between 12 and 15 years of age.
“In a previous study [23] we found a strong influence of age on facial soft tissue morphology between 12 and 15 years of age.”
From Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data (2019) · cites this paper as [23]
Our previous study dealt with longitudinal facial growth of juveniles aged between 12 and 15 years, was focused on ontogenetic trajectories and proposed an age progression model.
“Our previous study [23], which dealt with longitudinal facial growth of juveniles aged between 12 and 15 years, was focused on ontogenetic trajectories and proposed an age progression model.”
From Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data (2019) · cites this paper as [23]
Progress in computer science and improvements to medical imaging technologies have led to the development of computer systems for forensic facial reconstruction or for three-dimensional modelling of facial ageing using longitudinal and cross-sectional data.
“Progress in computer science and improvements to medical imaging technologies have led to the development of computer systems for forensic facial reconstruction [21,22] or for three-dimensional modelling of facial ageing using longitudinal [23,24] and cross-sectional data [25].”
From Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data (2019) · cites this paper as [23]
In a previous study [23], we reported differences in facial morphology and divergences in growth trajectories between the two sexes over the observation period.
“We reported differences in facial morphology as well as divergences in growth trajectories between the two sexes over the observation period.”
From Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data (2019) · cites this paper as [23]
The algorithm for facial prediction based on longitudinal 3D data was first presented in a previous study [23].
“The algorithm for facial prediction, which was based on longitudinal 3D data, was first presented in our previous study [23] a few years ago.”
From Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data (2019) · cites this paper as [23]
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Cited by
Sorted by citations
Koudelová, J., Hoffmannová, E., Dupej, J., & Velemínská, J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212618 · Cited by 33
Self-citation
Metadata
- Authors
- Koudelová, J., Dupej, J., Brůžek, J., Sedlak, P., & Velemínská, J.
- Venue
- Forensic Science International
- Publisher
- Elsevier BV
- Published
- 2015-03
- Volume
- 248
- Pages
- 33-40
- ISSN
- 0379-0738
- Cited by
- 28
- References
- 0
- Language
- en
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