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In this paper, we show how a dense surface point distribution model of the human face can be computed and demonstrate the usefulness of the high-dimensional shape-space for expressing the shape changes associated with growth and aging. We show how average growth trajectories for the human face can be computed in the absence of longitudinal data by using kernel smoothing across a population. A training set of three-dimensional surface scans of 199 male and 201 female subjects of between 0 and 50 years of age is used to build the model.

Original publication




Journal article


IEEE Trans Med Imaging

Publication Date





747 - 753


Adolescent, Adult, Aging, Algorithms, Cephalometry, Child, Child, Preschool, Face, Facies, Female, Head, Humans, Imaging, Three-Dimensional, Infant, Infant, Newborn, Male, Maxillofacial Development, Middle Aged, Models, Biological, Pattern Recognition, Automated, Sex Factors, Subtraction Technique