  Facial landmark localization is a fundamental module for pose-invariant face
recognition. The most common approach for facial landmark detection is cascaded
regression, which is composed of two steps: feature extraction and facial shape
regression. Recent methods employ deep convolutional networks to extract robust
features for each step, while the whole system could be regarded as a deep
cascaded regression architecture. In this work, instead of employing a deep
regression network, a Globally Optimized Dual-Pathway (GoDP) deep architecture
is proposed to identify the target pixels through solving a cascaded pixel
labeling problem without resorting to high-level inference models or complex
stacked architecture. The proposed end-to-end system relies on distance-aware
softmax functions and dual-pathway proposal-refinement architecture. Results
show that it outperforms the state-of-the-art cascaded regression-based methods
on multiple in-the-wild face alignment databases. The model achieves 1.84
normalized mean error (NME) on the AFLW database, which outperforms 3DDFA by
61.8%. Experiments on face identification demonstrate that GoDP, coupled with
DPM-headhunter, is able to improve rank-1 identification rate by 44.2% compared
to Dlib toolbox on a challenging database.
