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Facial landmark localization is a fundamental module for pose-invariant facerecognition. The most common approach for facial landmark detection is cascadedregression, which is composed of two steps: feature extraction and facial shaperegression. Recent methods employ deep convolutional networks to extract robustfeatures for each step, while the whole system could be regarded as a deepcascaded regression architecture. In this work, instead of employing a deepregression network, a Globally Optimized Dual-Pathway (GoDP) deep architectureis proposed to identify the target pixels through solving a cascaded pixellabeling problem without resorting to high-level inference models or complexstacked architecture. The proposed end-to-end system relies on distance-awaresoftmax functions and dual-pathway proposal-refinement architecture. Resultsshow that it outperforms the state-of-the-art cascaded regression-based methodson multiple in-the-wild face alignment databases. The model achieves 1.84normalized mean error (NME) on the AFLW database, which outperforms 3DDFA by61.8%. Experiments on face identification demonstrate that GoDP, coupled withDPM-headhunter, is able to improve rank-1 identification rate by 44.2% comparedto Dlib toolbox on a challenging database.

GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild