2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM)

1. Hybrid Clustering-based 3D Face Modeling upon
Non-Perfect Orthogonality of
Frontal and Profile Views
Alireza Ghahari, Mohsen Mosleh
2010 International Conference on Computer Information Systems and Industrial
Management Applications (CISIM)

2. Outline
 Introduction
 Method:
Feature Global Matching
Feature Extraction In The Frontal View
3D Computations In The Profile Views
Generic Model Modification And Texture Mapping

3. Introduction

4. Feature Global Matching
Preprocessing Feature Clusters
Input SCDA
Extraction
1. MIT-CBCL database (frontal view and side view)
2. Histogram equalization
3. A knowledge-based approach to develop a method for
micro features assembly localization.
4. Sequential Cluster Detection Algorithm

5. Sequential Cluster Detection Algorithm

6. Feature Extraction In The Frontal
View
• Extract allotted number of feature points, those which are
the most characteristic points used to represent a face.
• Shows 60 marked feature points in the frontal view
• Shows 31 corresponding feature points in the visible profile
view.

7. • Using an edge detector operator, We provide edge linking for
the Canny-processed images as a by-product of one double
thresholding scheme to reduce the effect of false positive
errors.

8.  Double thresholding

9. 3D Computations In The Profile
Views
Make correspondences between 2D feature points, (X,
Y), on frontal view and ones on the visible profile view,
(Z, Y).
We then make use of symmetry between facial
features which scatter at each side of the face and
the resulted coordinates from the frontal view and the
visible profile view to estimate the Z coordinate of
features in the hidden profile view.

10. - A visible Profile View
• In order to minimize the errors that may be introduced by
the orthogonality constraint, we utilize an algorithm on
detected 2D features aiming to decrease the dependency
of orthogonality of face images.
• Using a 3*3 intensity correlation
kernel, the algorithm self organizes
the margin ‘d’ in order to force the
solution Z coordinate to lie toward
the middle of the d=0 solution region
in hopes of improving generalization
of resulting depth estimator.

12. Generic Model Modification And
Texture Mapping
• Using NURBS curve segments for vertices definition and
Delaunay triangulation solution as the connection principle, a
modified version of the wellknown Candide model with 140
vertices and 264 surfaces has been designed.
※(NURBS，nonuniform rational B-splines）
• In order to reduce the effects of
non-perfect orthogonality, a two
step process should be considered
to provide properly smooth texture
image:

13. • 1) Image deformation: Defining two feature lines(right and
left lines) on the frontal image, the profile image is deformed
to be connected to the frontal image. The profile image is
deformed with translation to match to the right feature line,
while flipping it across to the vertical axis matches the
deformed profile image to be connected to the left feature
line.
• 2) Image mosaic: Mosaicing of boundaries between the three
combined images has been done to resist non-coherent
luminance. The mosaicing is obtained using three-level
wavelet packet decomposition.

15. End