Literature DB >> 25576677

Modelling of facial growth in Czech children based on longitudinal data: Age progression from 12 to 15 years using 3D surface models.

Jana Koudelová1, Ján Dupej2, Jaroslav Brůžek3, Petr Sedlak4, Jana Velemínská4.   

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.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  3D imaging; Age-progression; Facial growth; Forensic anthropology population data; Longitudinal study

Mesh:

Year:  2014        PMID: 25576677     DOI: 10.1016/j.forsciint.2014.12.005

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  6 in total

1.  Combined soft and skeletal tissue modelling of normal and dysmorphic midface postnatal development.

Authors:  Amel Ibrahim; Michael Suttie; Neil W Bulstrode; Jonathan A Britto; David Dunaway; Peter Hammond; Patrizia Ferretti
Journal:  J Craniomaxillofac Surg       Date:  2016-09-02       Impact factor: 2.078

2.  An exploration of adolescent facial shape changes with age via multilevel partial least squares regression.

Authors:  D J J Farnell; S Richmond; J Galloway; A I Zhurov; P Pirttiniemi; T Heikkinen; V Harila; H Matthews; P Claes
Journal:  Comput Methods Programs Biomed       Date:  2021-01-08       Impact factor: 5.428

3.  Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data.

Authors:  Jana Koudelová; Eva Hoffmannová; Ján Dupej; Jana Velemínská
Journal:  PLoS One       Date:  2019-02-22       Impact factor: 3.240

4.  Workflow and Strategies for Recruitment and Retention in Longitudinal 3D Craniofacial Imaging Study.

Authors:  Rafael Denadai; Junior Chun-Yu Tu; Ya-Ru Tsai; Yi-Ning Tsai; Emma Yuh-Jia Hsieh; Betty Cj Pai; Chih-Hao Chen; Alex Kane; Lun-Jou Lo; Pang-Yun Chou
Journal:  Int J Environ Res Public Health       Date:  2019-11-12       Impact factor: 3.390

5.  Morphometric approach to 3D soft-tissue craniofacial analysis and classification of ethnicity, sex, and age.

Authors:  Olalekan Agbolade; Azree Nazri; Razali Yaakob; Abdul Azim Ghani; Yoke Kqueen Cheah
Journal:  PLoS One       Date:  2020-04-09       Impact factor: 3.240

6.  A longitudinal study of facial growth of Southern Chinese in Hong Kong: Comprehensive photogrammetric analyses.

Authors:  Yi Feng Wen; Hai Ming Wong; Colman Patrick McGrath
Journal:  PLoS One       Date:  2017-10-20       Impact factor: 3.240

  6 in total

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