![](bg4.png)
Figure 4: Texture mapping and 3D model generation.
mapping is performed using the OpenGL library of
computer graphics.
The results show that the proposed strategy, con-
sisting of incorporating prior knowledge in the dis-
parity estimation process, is robust and accurate. It
improves the result of general correlation based meth-
ods by considering the face shape and its topological
regions, while maintaining its real-time suitability.
5 CONCLUSIONS AND
PERSPECTIVES
This paper presents an original attempt to a practi-
cal face depth estimation in passive stereoscopic sys-
tem. Unlike other general methods used for dispar-
ity calculation for any object, we introduced a spe-
cific method for depth estimation of face that uses the
shape characteristics of the human face, obtained by
adjusting the form of an active model, to improve re-
sult of the correlation-based method. Our method en-
hanced the classical correlation based method for dis-
parity calculation, in terms of depth estimation effi-
ciency, with maintaining its real-time suitability. The
experimental results show that the proposed algorithm
produces a smooth and dense 3D point cloud model of
human face, applicable to a wide range of real-time
3D face reconstruction situations.
Our approach also opens up many perspectives for
improvement and expansion. The estimation of the
sparse disparity can be improved by using other ver-
sions of the Active Shape Model used in our work.
For instance, Active Appearance Models (Cootes
et al., 2001) is likely to give more successful adjust-
ments because they use the texture information or the
3D Active Appearance Models (Xiao et al., 2004)
which is robust to pose variation. The symmetry pro-
priety of the face can also be incorporated in the esti-
mation process to further improve the results. Finally,
it would be interesting to test our method on a stereo
face database with ground truth. However, existing
databases usually contain scenes and objects. For this,
we plan to create a specific database of stereoscopic
faces with a ground truth to evaluate our method in a
complete way.
REFERENCES
Choi, J., Medioni, G., Lin, Y., Silva, L., Regina, O., Pam-
plona, M., and Faltemier, T. (2010). 3d face recon-
struction using a single or multiple views. In Pattern
Recognition (ICPR), 2010 20th International Confer-
ence on, pages 3959 –3962.
Chow, C. and Yuen, S. (2009). Recovering shape by shading
and stereo under lambertian shading model. Interna-
tional journal of computer vision, 85(1):58–100.
Chowdhury, A. K. R. and Chellappa, R. (2003). Face re-
construction from monocular video using uncertainty
analysis and a generic model. Computer Vision and
Image Understanding, 91:188–213.
Cootes, T., Edwards, G., and Taylor, C. (2001). Active ap-
pearance models. Pattern Analysis and Machine In-
telligence, IEEE Transactions on, 23(6):681–685.
Cryer, J., Tsai, P., and Shah, M. (1995). Integration of
shape from shading and stereo* 1. Pattern recogni-
tion, 28(7):1033–1043.
Furukawa, Y. and Ponce, J. (2010). Accurate, Dense,
and Robust Multiview Stereopsis. Pattern Analy-
sis and Machine Intelligence, IEEE Transactions on,
32(8):1362–1376.
Heo, Y. S., Lee, K. M., and Lee, S. U. (2011). Robust stereo
matching using adaptive normalized cross-correlation.
IEEE Transactions on Pattern Analysis and Machine
Intelligence, 33:807–822.
Hirschmuller, H. (2001). Improvements in real-time
correlation-based stereo vision. In Stereo and Multi-
Baseline Vision, 2001.(SMBV 2001). Proceedings.
IEEE Workshop on, pages 141–148. IEEE.
Lengagne, R., Fua, P., and Monga, O. (2000). 3d stereo
reconstruction of human faces driven by differential
constraints. Image and Vision Computing, 18(4):337–
343.
Mallick, S. P. and Trivedi, M. (2003). Parametric face mod-
eling and affect synthesis. In Proceedings of the 2003
International Conference on Multimedia and Expo -
Volume 2, ICME ’03, pages 225–228, Washington,
DC, USA. IEEE Computer Society.
Milborrow, S. and Nicolls, F. (2008). Locating facial fea-
tures with an extended active shape model. Computer
Vision–ECCV 2008, pages 504–513.
Scharstein, D. and Szeliski, R. (2002). A taxonomy and
evaluation of dense two-frame stereo correspondence
algorithms. Int. J. Comput. Vision, 47:7–42.
Wu, X., Dai, C., and Liu, J. (2008). A novel approach
for face recognition based on stereo image processing
algorithm. In Audio, Language and Image Process-
ing, 2008. ICALIP 2008. International Conference on,
pages 1245 –1249.
Xiao, J., Baker, S., Matthews, I., and Kanade, T. (2004).
Real-time combined 2d+3d active appearance models.
In IEEE Conference on Computer Vision and Pattern
Recognition, volume 2, pages 535 – 542.
Zheng, Y., Chang, J., Zheng, Z., and Wang, Z. (2007). 3d
face reconstruction from stereo: A model based ap-
proach. In Image Processing, 2007. ICIP 2007. IEEE
International Conference on, pages III –65 –III –68.
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