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


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Thesis - Master's thesis | @MastersThesis | Masterarbeit


Author
Author(s)*:Scherbaum, Kristina
BibTeX citekey*:Scherbaum2007
Language:English

Title, School
Title*:Face Recognition and Growth Prediction using a 3D Morphable Face Model
School:Universität des Saarlandes
Type of Thesis*:Master's thesis
Month:October
Year:2007


Note, Abstract, Copyright
LaTeX Abstract:We present two different techniques and applications that are based on the 3D Morphable Face Model.

In the first part of this thesis, we develop a new top-down approach to 3D data analysis by fitting a 3D Morphable Face Model to 3D scans of faces. The algorithm is specifically designed for scans which were recorded in a perspective projection. In an analysis-by-synthesis approach, shape, texture, pose and illumination are optimized simultaneously. Starting from raw 3D scans, the algorithm determines a PCA-based representation which fits the scan best. Also, fragmentary surfaces are completed and correspondence to a reference face of the morphable model is established. Simultaneously, illumination conditions are estimated in an explicit simulation that involves specular and diffuse components. The effects of lighting and shading are removed to obtain an illumination corrected texture which stores the diffuse reflectance in each point of the facial surface. We use the algorithm as a core component in 3D face recognition on a subset of the FRGC database of scans.
In part two of this thesis we explore the growth of 3D faces, represented in a 3D Morphable Face Model. Assuming that 3D faces follow curved trajectories in face space as they age, we present a novel algorithm that computes individual aging trajectories for given faces. From a database of 3D scans of teenagers and adults, we learn an non-linear function, that assigns an age to each face vector using support vector regression. Computing the gradient of this function leads us to trajectories that describe the direction of growth in face space. Starting from photographs of faces we apply the aging prediction to images of faces by reconstructing a 3D model from the input image and applying the aging transformation on both shape and texture. The resulting face model is rendered back into the same image or into images of other individuals at the appropriate ages, for example images of older children. Also we may compute a variety of possible appearances by changing attributes such as haircut, cloth or background. Among other applications, our system can help to find missing children.

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Referees, Status, Dates
Supervisor:Volker Blanz, Universität Siegen and Hans-Peter Seidel, Universität des Saarlandes
Status:Completed
Date Kolloquium:30 October 2007

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{Scherbaum2007,
AUTHOR = {Scherbaum, Kristina},
TITLE = {Face Recognition and Growth Prediction using a 3D Morphable Face Model},
SCHOOL = {Universit{\"a}t des Saarlandes},
YEAR = {2007},
TYPE = {Master's thesis}
MONTH = {October},
}


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Entry last modified by Kristina Scherbaum, 02/28/2008
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Editor(s)
Kristina Scherbaum
Created
12/11/2007 05:01:52 PM
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Editor
Kristina Scherbaum



Edit Date
12/11/2007 05:01:53 PM