Robust face alignment by dual-attentional spatial-aware capsule networks

Abstract

Face alignment in-the-wild still faces great challenges due to that i) partial occlusion blurs the inter-features spatial relations of faces and ii) traditional CNN makes the network more difficult to capture the spatial positional relations between landmarks. To address the issues above, we propose a face alignment algorithm named Dual-attentional Spatial-aware Capsule Network (DSCN). Firstly, the spatial-aware module builds a more accurate inter-features spatial constrained model with the hourglass capsule network (HGCaps) as the backbone, which can effectively enhance its robustness against occlusions. Then, two sorts of attention mechanisms, namely capsule attention and spatial attention, are added to the attention-guided module to make the network focus more on the advantageous features and suppress other unrelated ones for more effective feature recalibration. Our method achieves 1.08% failure rate on the COFW dataset, which is much lower than the current state-of-the-art algorithms. The mean error under 300W dataset and WFLW dataset are respectively 3.91% and 5.66%, which shows that DSCN is more robust to occlusion and outperforms state-of-the-art methods in the literature.