  Multiview 3D face modeling has attracted increasing attention recently and
has become one of the potential avenues in future video systems. We aim to make
more reliable and robust automatic feature extraction and natural 3D feature
construction from 2D features detected on a pair of frontal and profile view
face images. We propose several heuristic algorithms to minimize possible
errors introduced by prevalent nonperfect orthogonal condition and noncoherent
luminance. In our approach, we first extract the 2D features that are visible
to both cameras in both views. Then, we estimate the coordinates of the
features in the hidden profile view based on the visible features extracted in
the two orthogonal views. Finally, based on the coordinates of the extracted
features, we deform a 3D generic model to perform the desired 3D clone
modeling. Present study proves the scope of resulted facial models for
practical applications like face recognition and facial animation.
