Face Alignment Using K-Cluster Regression Forests With Weighted Splitting

In this letter, we present a face alignment pipeline based on two novel methods: weighted splitting for K-cluster Regression Forests (KRF) and three-dimensional Affine Pose Regression (3D-APR) for face shape initialization. Our face alignment method is based on the Local Binary Feature (LBF) 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 Histogram of Oriented Gradients (PHOG) features. We also use KRFWS to perform APR and 3D-APR, which intend to improve the face shape initialization. APR applies a rigid 2-D 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 3-D 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 Intelligent Behaviour Understanding Group (IBUG) dataset, and state-of-the-art accuracy on the entire 300-W dataset.