Image Processing Reference

In-Depth Information

6 Face Tracking

The use of our MS-RNN method for face tracking relies on the fact that the skin color is in-

variant to face orientation and is insensitive to partial occlusion. Also, our system proved in-

sensitive to variations of scene conditions, such as the presence of a complex background and

uncontrolled illumination. Based on these considerations, we applied the MS-RNN method at

to predict the next face-detection window and smooth the tracking trajectory. Face detection

is performed within a predicted window instead of an entire image region to reduce compu-

tation costs. The
x
−
y
coordinates and height of the face region are initially set to the values

given by the face-detection process, while the velocity values of the state vector are set to 1.

The face motion model used in our tracking method can be defined by the following set of

space-state equations:

where
x
k
represents the state vector at the time
k
, characterized by five parameters consisting

of the
x
−
y
coordinates of the center point of the face region (
c
x
,
c
y
), the velocity in the
x
and
y

directions (
v
x
,
v
y
), and the height
H
k
of the face-bounded region. The width of the face-bounded

region is always assumed to be 0.75 times the size of the calculated height. The transition mat-

rix,
Φ
, which relates the current state to the predicted state after the time interval Δ
t
is given

as:

The vector
z
k
∈
3
represents the face position and height observed with the observation

matrix:

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