Age‐related craniofacial differences based on spatio‐temporal face image atlases. Issue 9 (20th June 2019)

• Record Type: Journal Article
• Authors: Xavier, Igor R.R.; Giraldi, Gilson A.; Gibson, Stuart James; Gattas, Gilka J.F.; Rueckert, Daniel; Thomaz, Carlos E.
• Is Part Of: IET image processing. Volume 13:Issue 9(2019)
• Page Start: 1561
• Page End: 1568
• Publication Date: 2019-06-20
• Subjects: image registration -- genetics -- face recognition -- spatiotemporal phenomena; realistic unbiased face models -- age‐intervals -- age‐related craniofacial differences -- spatio‐temporal face image atlases -- genetics -- spatial face image models -- age progression information -- facial landmark positioning -- nonrigid registration -- age‐dependent face -- facial differences -- craniofacial metrics -- distance metrics -- abnormal craniofacial development; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6271
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : A number of studies have been developed recently in order to explore associations between craniofacial differences and genetics. Most of these works have been based on spatial face image models, adjusted for the counter effects of age. This approach provides a limited understanding of normal and abnormal craniofacial development owing to the lack of age progression information. Here, the authors propose and implement an imaging framework that combines facial landmark positioning, non‐rigid registration, novel age‐dependent face modelling and common distance metrics to disclose the most facial differences that vary across the time due to the subjects' age. All the experiments carried out and corresponding results presented here are based on a database comprising ordinary two‐dimensional (2D) frontal face images of Down Syndrome (DS) and control sample groups. A number of craniofacial metrics have been successfully identified that highlight statistically significant and clinically relevant differences between the controls and the faces associated with DS within the age range from 1 to 18 years old, producing realistic unbiased face models with similar level of detail at all age‐intervals, despite the small sample size available.