Appearance Model (AAM)
In face recognition application, accurate face
alignment has determinative effect. Active Appearance Model (AAM) is one of
the most studied methods for accurate locating objects. When applying Active
Appearance Model, firstly we collect enough face
images with various shapes as training set.
Then we use a set of points to annotate face shape, so face shape can be
represented by the coordinates of these landmarks.
Construction of mean shape
After a series of transformation such as Principal
Component Analysis, the mean shape of all the faces can be obtained to
construct shape model for face alignment.
Given a new face image, we estimate the
model’s initial position, compute the suggested movements, then
we can get a good face alignment result.
of face alignment
We mainly use
shape parameters and appearance parameters obtained
by alignment for real time video tracking and pose
estimate. The speed of this algorithm is 123
frames/second. So it is applicable in real time face
tracking. The pose estimate we
implement contains: horizontal and vertical
movements; forward or backward from camera; rotation
angle; eyes and mouth state.
of real time tracking and pose estimate
Result analysis of one frame