  In this work we present a face alignment pipeline based on two novel methods:
weighted splitting for K-cluster Regression Forests and 3D Affine Pose
Regression for face shape initialization. Our face alignment method is based on
the Local Binary Feature framework, where instead of standard regression
forests and pixel difference features used in the original method, we use our
K-cluster Regression Forests with Weighted Splitting (KRFWS) and Pyramid HOG
features. We also use KRFWS to perform Affine Pose Regression (APR) and
3D-Affine Pose Regression (3D-APR), which intend to improve the face shape
initialization. APR applies a rigid 2D transform to the initial face shape that
compensates for inaccuracy in the initial face location, size and in-plane
rotation. 3D-APR estimates the parameters of a 3D transform that additionally
compensates for out-of-plane rotation. The resulting pipeline, consisting of
APR and 3D-APR followed by face alignment, shows an improvement of 20% over
standard LBF on the challenging IBUG dataset, and state-of-theart accuracy on
the entire 300-W dataset.
