Construction and visualisation of three-dimensional facial statistics

This paper presents a new method for the construction of three-dimensional (3D) probabilistic facial averages and demonstrates the potential for applications in clinical craniofacial research and patient assessment. Averages are constructed from a database of registered laser-range scans and photogr...

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Bibliographic Details
Published inComputer methods and programs in biomedicine Vol. 63; no. 1; pp. 9 - 20
Main Authors Tiddeman, Bernard, Duffy, Neil, Rabey, Graham
Format Journal Article
LanguageEnglish
Published Shannon Elsevier Ireland Ltd 01.08.2000
Elsevier Science
Subjects
Adult
Biological and medical sciences
Cephalometry - methods
Computerized, statistical medical data processing and models in biomedicine
Databases, Factual
Face - anatomy & histology
Facial averages
Facial statistics
Feasibility Studies
Female
Humans
Image Processing, Computer-Assisted - methods
Lasers
Male
Medical computing and teaching
Medical sciences
Morphanalysis
Normal Distribution
Photography
Probability
Reference Values
Reproducibility of Results
Sex Characteristics
Three-dimensional medical imaging
Tomography, X-Ray Computed
Morphanalysis
Facial averages
Three-dimensional medical imaging
Facial statistics
Human
Statistical method
Biomedical data processing
Averaging method
Texture analysis
Probabilistic approach
Morphological analysis
Laser
Craniofacial
Tridimensional image
Pattern extraction
Image reconstruction
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Summary:This paper presents a new method for the construction of three-dimensional (3D) probabilistic facial averages and demonstrates the potential for applications in clinical craniofacial research and patient assessment. Averages are constructed from a database of registered laser-range scans and photographic images using feature based image warping. Facial features are extracted using a template of connected contours, adapted to each subject interactively using snakes. Each subject's images are warped to the average template shape and the mean depth, colour and covariance matrix is found at each point. Statistical comparison of individuals with an average or between two averages is visualised by converting the probabilities to a coloured texture map.
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ISSN:0169-2607
1872-7565
DOI:10.1016/S0169-2607(00)00072-9