Antonio Colmenarez, Brendan J. Frey and Thomas S. Huang 1999.
A probabilistic framework for embedded face and facial expression
recognition.
In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition 1999,
Ft. Collins, CO. IEEE Computer Society Press: Los Alamitos, CA.
We present a Bayesian recognition framework in which a model of the
whole face is enhanced by models of facial feature positions and
appearances. Face recognition and facial expression recognition are
carried out using maximum likelihood decisions. The algorithm finds the
model and facial expression that maximizes the likelihood of a test
image. In this framework, facial appearance matching is improved by
facial expression matching. Also, changes in facial features due to
expressions are used together with facial deformation patterns to
jointly perform expression recognition.
In our current implementation, the face is divided into 9 facial
features grouped in 4 regions which are detected and tracked
automatically in video segments. The feature images are modeled using
Gaussian distributions on a principal component sub-space. The training
procedure is supervised: we use video segments of people in which the
facial expressions have been segmented and labeled by hand. We report
results on face and facial expression recognition using a video database
of 18 people and 6 expressions.
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