matching 
about ten 
region to 
ouple of 
ompletely 
ne, about 
oximately 
les of the 
acquired 
each side 
> end, the 
puted by 
alibration 
) points is 
nm in the 
ign 
joints), 
its) 
  
As shown in figure 13 (top), the point cloud is very dense 
(45,000 points) and the region of overlap of the two joined data 
set can be observed in the center line of the face. To overcome 
the redundant data and remove eventual outliers, Gaussian 
filters (Borghese and Ferrari, 2000) are applied to the 3-D point 
cloud and the data is afterwards thinned (see figure 13 bottom). 
For surface measurement purposes, the computed 3-D point 
cloud is satisfactory. In case of visualization, a complete model 
of the face with texture has to be produced. A meshed surface is 
therefore generated from the 3-D point cloud by 2.5-D 
Delauney triangulation and to achieve photorealistic 
visualization, the natural texture acquired by the color video 
camera is draped over the model of the face. Figure 14 shows 
the surface model, the texture image and two views of the 
resulted face model with texture, figure 15 shows two other 
examples of face models. 
   
  
Figure 14. Photorealistic visualization; top: shaded surface 
model and texture image; bottom: face model with texture 
3. CONCLUSIONS 
A process for an automated measurement of the human face 
from multi-images acquired by five synchronized CCD cameras 
has been presented. The main advantages of this method are its 
flexibility, the reduced costs of the hardware and the possibility 
to perform surface measurement of dynamic events. 
ACKNOWLEDGEMENT 
The work reported here was funded in part by the Swiss 
National Science Foundation. 
    
REFERENCES 
Banda, F.A.S. et al, 1992. Automatic Generation of Facial 
DEMs. In: Int. Archives of Photogrammetry and Remote 
Sensing, Vol. XXIX, Part BS, pp. 893-896. 
Blanz, V. and Vetter, T., 1999. A Morphable Model for the 
Synthesis of 3D Faces. In: SIGGRAPH'99 Conf. Proc., pp. 187- 
194. 
Borghese, A. and Ferrari, S., 2000. A Portable Modular System 
for Automatic Acquisition of 3-D Objects. IEEE Trans. on 
Instrumentation and Measurement, 49(5), pp. 1128-1136. 
Brown, D.C., 1971. Close-Range Camera Calibration. 
Photogrammetric Engineering and Remote Sensing, 37(8), pp. 
855-866. 
CNN, 2001. Facing Up to Airport Security Fear. 
http://europe.cnn.com/2001/US/09/28/facial.recognition.QandA 
(accessed 11 June 2002) 
Cyberware, 2002. Head and Face Color 3D Scanner Model 
3030. http://www.cyberware.com/products/psInfo.html 
(accessed 11 June 2002) 
D'Apuzzo, N., 1998. | Automated Photogrammetric 
Measurement of Human Faces. In: Int. Archives of 
Photogrammetry and Remote Sensing, Hakodate, Japan, Vol. 
XXXII, Part B5, pp. 402-407. 
D'Apuzzo, N., 2001. Photogrammetric Measurement and 
Visualisation of Blood Vessel Branching Casting: A Tool for 
Quantitative Accuracy Tests of MR-, CT- and DS- 
Angiography. In: Videometrics and Optical Methods for 3D 
Shape Measurement, San Jose, USA, Proc. of SPIE, Vol. 4309, 
pp. 204-211. 
D'Apuzzo, N., 2002. Surface measurement and surface tracking 
of human body parts from multi image video sequences. /SPRS 
Journal of Photogrammetry and Remote Sensing, 56(4). 
De Carlo, D. et al, 1998. An Anthropometric Face Model 
Using Variational Techniques. In: SIGGGRAPH'98 Conf. Proc., 
pp. 67-74. 
Foerstner, W. and Guelch, E., 1987. A Fast Operator for 
Detection and Precise Location of Distinct Points, Corners and 
Centres of Circular Features. In: Proc. of the Intercommission 
Conference on Fast Processing of Photogrammetric Data, 
Interlaken, Switzerland, pp. 281-305. 
Fua, P., 2000. Regularized Bundle-Adjustment to Model Heads 
from Image Sequences without Calibration Data. Int. Journal of 
Computer Vision, 38(2), pp. 153-171. 
Figure 15. Photorealistic visualization; two other examples of face models 
-245- 
EAP PSI AO es