We propose a facial landmarks detector, in which a partbased
model is incorporated with holistic face information.
In the part-based model, the face is modeled by the appearance
of different face parts and their geometric relation.
The appearance is described by pixel normalized difference
descriptor. This descriptor is the lowest computational
complexity as compared with existing state-of-theart
while it has a similar accuracy. On the other hand, to
model the geometric relation between the face parts, the
complex Bingham distribution is adapted. This is because
the complex Bingham distribution has a symmetric property
so it is invariant to rotation, scale, and translation.
After that the global information is incorporated with the
local part-based model using a regression model. The regression
model estimates the displacement to the final face
shape model. The the proposed detector is evaluated on two
datasets. Experimental results show that it outperforms the
state-of-the-art approaches in detecting facial landmarks
accurately.