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MPI-INF D4 Publications :: Thesis :: Scherbaum, Kristina


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Thesis - Diploma Thesis | | Diplomarbeit


Author
Author(s)*:Scherbaum, Kristina
BibTeX citekey*:Scherbaum2005
Language:English

Title, School
Title*:Learning Based Prediction and 3D-Visualisation of Children’s Facial Growth
School:Fachhochschule Stuttgart, Hochschule der Medien
Type of Thesis*:Diploma Thesis
Month:March
Year:2005


Note, Abstract, Copyright
LaTeX Abstract:Based on a novel, example-based approach for studying how children’s faces change as they grow,

we present an automated algorithm for the prediction of children’s facial growth.
A single image of an arbitrary face at its present appearance is sufficient to estimate an age-progressed
3D face model of the individual. By extracting and applying growth vectors from a previously acquired
database, the face can be transformed into its individual older or younger appearance.
The database is a combination of an existing set of 3D laser scans of 200 adult human faces and
two newly acquired 3D databases of baby and teenager faces. Those are generated from 3D laser
scans and stereo images, respectively. By incorporating the data into a morphable 3D face model,
a homogeneous face space is constructed, which contains 523 3D face models of adults, teenagers
and babies in a vector representation that involves dense point-to-point correspondence between
individual faces.
For the baby face models, additional anthropometric measurements of the facial soft tissue are
performed by a novel, automated algorithm. The results provide a longitudinal study of baby faces,
which is intended to assist in detecting anomalies of the facial growth at early stages of development.
The additional shape and texture vectors for children and a subsequent Principal Component Analysis
improve the reconstruction results of the morphable 3D face model when matching it to infant faces.
Moreover, a subdivision of the database into age groups makes it possible to describe the individual
growth of a face continuously, starting from a minimum age of 3 months to a maximum age of
35 years. In the vector space representation, growth is modelled by a piecewise linear function that
approximates a desired target age for an individual face by learning the growth from the given sample
set of 523 faces.

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Referees, Status, Dates
Supervisor:Bernhard Eberhardt, Hochschule der Medien Stuttgart and Volker Blanz, MPI Informatik Saarbrücken
Status:Completed
Date Kolloquium:2 March 2005

Correlation
MPG Unit:Max-Planck-Institut für Informatik
MPG Subunit:Computer Graphics Group
Audience:experts only
Appearance:MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort


BibTeX Entry:
@MASTERSTHESIS{Scherbaum2005,
AUTHOR = {Scherbaum, Kristina},
TITLE = {Learning Based Prediction and 3D-Visualisation of Children’s Facial Growth},
SCHOOL = {Fachhochschule Stuttgart, Hochschule der Medien},
YEAR = {2005},
TYPE = {Diploma thesis}
MONTH = {March},
}


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Entry last modified by Kristina Scherbaum, 12/11/2007
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Editor(s)
Kristina Scherbaum
Created
12/11/2007 05:05:58 PM
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Editor
Kristina Scherbaum



Edit Date
12/11/2007 05:05:59 PM