Figure 9. Regularization; left: seed points and matched points in 
the template image; right: after regularization of the grid 
For the definition of the filter, the smoothed characteristic of the 
surface of the human face is taken in account: as shown in 
figure 10, the transformed image patches of neighboring points 
belonging to a common smoothed surface have similar shapes. 
A neighborhood filter is therefore applied to the set of matched 
points checking for the local uniformity of the shape of the 
transformed image patches. 
  
Figure 10. Points matched in the neighborhood 
The complete matching process (definition of seed points, 
automatic matching, filtering) is flexible and can also be 
performed without orientation and calibration information. This 
functionality can be useful, for example, if the orientation is not 
accurate enough or unknown. In these special cases, only the 
image information is used by the least squares matching 
algorithm. Obviously, the robustness of the result of the process 
decreases; however the quality of the set of matched points 
remains satisfactory. 
A. dedicated software was developed for the face measurement 
process. Figures 11 and 12 show its user friendly graphical 
interface. 
  
  
  
  
  
  
  
  
  
  
  
Figure 11. Graphical user interface of the face 
measurement software; seed points definition 
  
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Figure 12. Graphical user interface of the face 
measurement software; matching results and 
visualization of the computed 3-D point cloud 
The required intervention of the operator for the matching 
process is reduced to the semi-automatic definition of about ten 
seed points and the selection of a contour of the region to 
measure. The operation can be performed in a couple of 
minutes, then the process will continue completely 
automatically. On a Pentium III 600 MHz machine, about 
20,000 points are matched on half of the face in approximately 
10 minutes. 
2.3 Modeling and visualization 
Since the human face is a steep surface and both sides of the 
face are not visible to the same camera, the five acquired 
images are used as two separate set of triplets, one for each side 
of the face. They are processed separately and at the end, the 
results are merged into a single data set. 
The 3-D coordinates of the matched points are computed by 
forward ray intersection using the orientation and calibration 
data of the cameras. The achieved accuracy of the 3-D points is 
about 0.2 mm in the sagittal direction and about 0.1 mm in the 
lateral direction. 
t hasc ; 
  
      
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Figure 13. Top: measured 3-D point cloud (45,000 points), 
bottom: after filtering and thinning (10,000 points) 
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