![](bg8.png)
comparison with ELITE2002 system with changes in
illumination. Sumarizing we obtain a better precision
and an extended trackability in all cases of strong il-
lumination changes.
Figure 6: Example of synthetic test for illumination change.
(a) shows the tracked face, (b) and (c) shows face normal-
izate without and with illumination adjusting.
6 CONCLUSION
Concluding, we developed a robust expression analy-
sis oriented face tracker with posture confidence eval-
uations, that makes the tracking good and very close
to ELITE2002 estimation. The algorithm proposed
is robust to face morphing and illumination changes
in spite of the difference between the 3D face model
and the real subject face. Our system performs good
results thanks to the correction techniques like the
mosaic ones and the dissimilarity analysis. We also
showed that this method permits to extracted many
measures linked to the AU that can be used for face
expression detection.
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FACE TRACKING ALGORITHM ROBUST TO POSE, ILLUMINATION AND FACE EXPRESSION CHANGES: A 3D
PARAMETRIC MODEL APPROACH
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