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Estimating average growth trajectories in shape-space using kernel smoothing.

Tim J Hutton1, Bernard F Buxton, Peter Hammond

  • 1Biomedical Informatics Unit, Eastman Dental Institute for Oral Health Care Sciences, University College London, 256 Gray's Inn Road, London WC1X 8LD, UK. T.Hutton@eastman.ucl.ac.uk

IEEE Transactions on Medical Imaging
|July 23, 2003
PubMed
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This study models human face shape changes during growth and aging using 3D scans. It reveals average facial development trajectories without needing long-term patient data, aiding in understanding facial morphology.

Area of Science:

  • Biomedical Engineering
  • Anthropometry
  • Computer Vision

Background:

  • Understanding human facial morphology changes over time is crucial for various applications.
  • Existing methods often require extensive longitudinal data, which is difficult to obtain.
  • High-dimensional shape-space analysis offers a novel approach to model complex biological structures.

Purpose of the Study:

  • To compute a dense surface point distribution model of the human face.
  • To demonstrate the utility of high-dimensional shape-space for analyzing facial growth and aging.
  • To derive average human face growth trajectories using cross-sectional data.

Main Methods:

  • Utilized a training dataset of 3D surface scans from 400 subjects (199 male, 201 female) aged 0-50 years.

Related Experiment Videos

  • Developed a dense surface point distribution model for comprehensive facial shape representation.
  • Applied kernel smoothing techniques to estimate average growth trajectories from population data.

    • Main Results:

    • Successfully computed a detailed 3D facial shape model.
    • Demonstrated that the high-dimensional shape-space effectively captures shape variations due to growth and aging.
    • Generated average facial growth trajectories by analyzing the population's shape-space.

    Conclusions:

    • The developed facial shape model and high-dimensional shape-space are effective tools for studying human facial development.
    • The method allows for the computation of average growth trajectories without longitudinal data, offering a valuable alternative.
    • This research provides insights into facial morphology changes across the lifespan, with potential applications in medicine and computer graphics.