Facial Gesture
Recognition
Abstract
I will present my work on facial gesture recognition. In facial gesture
recognition problem, different head poses will increase difficulty in
recognition. We use a head pose estimation method based on iterative
reweighted least-square to deal with this issue. After the recovery of head
pose, we extract the dense flows effected by the expression change. Besides,
the human face is divided into 9 regions according to its characteristics.
In each region, an affine motion model is fitted based on those flows and
parameters of the affine motion model are used for classification. The
classification framework consists of graphical models and SVM. Instead of
using single parametric distribution, we apply the Gaussian mixture to model
the interactions between different regions, and the EM algorithm is used for
training.