Facial Landmark Localization Based on Auto-Stacked Hourglass Network and Expectation Consensus

Zhibin Hong, Hanqi Guo, Ziyuan Guo, Yanqin Chen, Bi Li, Teng Xi

Abstract: A facial landmark localization method is introduced in this paper. It consists of several models that developed on the basis of stacked hourglass network and performs landmark localization from coarse to fine in two stages. The results are fused according to expectation consensus to remove outliers and it can alleviate the problem of inductive bias. We improve the alignment model with weighted heatmaps to get rid of the quantization errors and by applying auto ML to search better network structures under accurate guidance. Besides, the models are trained with face segmentation and a well-designed augmentation scheme. The method achieves the 1st place in the Grand Challenge of 106-point Facial Landmark Localization in ICME2019 with AUC of 84.01%.