Attention-Guided Coarse-to-Fine Network for 2D Face Alignment in the Wild

Methods based on stacked hourglass networks (SHNs) have achieved great progress in face alignment tasks. However, most of these algorithms have met with limited success in modeling correlations among features. Visual attention mechanisms have shown promise in terms of effectively understanding scenes in various computer vision tasks. In this paper, an attention-guided coarse-to-fine network (AGCFN) based on an attention mechanism is proposed for robust face alignment. Thus, the network is guided to emphasize key information while suppressing less important information. Meanwhile, the fusion of features from different levels is adopted to improve the information flow through the proposed network. Additionally, conditional random fields (CRFs) are introduced to model the spatial interactions between landmarks in the prediction maps. Experimental results obtained on the 300-W dataset, the 300-W private test set, and the WFLW dataset demonstrate the superiority of the proposed method in terms of accuracy and robustness.