  Face alignment has witnessed substantial progress in the last decade. One of
the recent focuses has been aligning a dense 3D face shape to face images with
large head poses. The dominant technology used is based on the cascade of
regressors, e.g., CNN, which has shown promising results. Nonetheless, the
cascade of CNNs suffers from several drawbacks, e.g., lack of end-to-end
training, hand-crafted features and slow training speed. To address these
issues, we propose a new layer, named visualization layer, that can be
integrated into the CNN architecture and enables joint optimization with
different loss functions. Extensive evaluation of the proposed method on
multiple datasets demonstrates state-of-the-art accuracy, while reducing the
training time by more than half compared to the typical cascade of CNNs. In
addition, we compare multiple CNN architectures with the visualization layer to
further demonstrate the advantage of its utilization.
