Score-Guided Face Alignment Network Under Occlusions.

Recent state-of-the-art landmark localization task are dominated by heatmap regression and fully convolutional network. In spite of its superior performance in face alignment, heatmap regression method has a few drawbacks in nature, such as do not follow shape constraint and sensitivity to partial occlusions. In this paper, we proposed a score-guided face alignment network that simultaneously outputs a heatmap and corresponding score map for each landmark. Rather than treating all predicted landmarks equally, a weight is assigned to each landmark based on the two relational maps. In this way, more reliable landmarks with strong local information are assigned large weights and the land-marks with small weights that may stay with occlusions can be inferred with the help of the reliable landmarks. Meanwhile, an exemplar-based shape dictionary is designed to take advantage of these landmarks with high score to infer the landmark with small score. The shape constraint is implicitly applied in this way. Thus our method demonstrates superior performance in detecting landmarks with extreme occlusions and improving overall performance. Experiment results on 300 W and COFW dataset show the effectiveness of the proposed method.