  Facial landmarks are highly correlated with each other since a certain
landmark can be estimated by its neighboring landmarks. Most of the existing
deep learning methods only use one fully-connected layer called shape
prediction layer to estimate the locations of facial landmarks. In this paper,
we propose a novel deep learning framework named Multi-Center Learning with
multiple shape prediction layers for face alignment. In particular, each shape
prediction layer emphasizes on the detection of a certain cluster of
semantically relevant landmarks respectively. Challenging landmarks are focused
firstly, and each cluster of landmarks is further optimized respectively.
Moreover, to reduce the model complexity, we propose a model assembling method
to integrate multiple shape prediction layers into one shape prediction layer.
Extensive experiments demonstrate that our method is effective for handling
complex occlusions and appearance variations with real-time performance. The
code for our method is available at
https://github.com/ZhiwenShao/MCNet-Extension.
