论文标题
传播网：传播点曲线以学习结构信息
PropagationNet: Propagate Points to Curve to Learn Structure Information
论文作者
论文摘要
深度学习技术极大地提高了面部比对算法的性能。但是，由于较大的可变性和缺乏样本，在不受约束的情况下的对齐问题，\ emph {e.g} \ oneDot较大的头姿势，夸张的表达和不均匀的照明仍然很大程度上尚未解决。在本文中，我们探讨了两个提案背后的本能和原因，即\ emph {i.e} \ oneDot繁殖模块和焦点翼损失，以解决问题。具体而言，我们提出了一种基于热图回归的新型结构注入的面部对齐算法，通过将具有标志性的热图传播到边界热图，该算法为进一步的注意力图生成提供了结构信息。此外，我们提出了用于采矿的焦点损失，并在野外状况下强调了困难的样本。此外，我们从其他领域采用了COORDCONV和抗恶化CNN等方法，这些方法解决了CNN的偏移变化问题以进行面部对齐。当在不同基准上实现广泛的实验时，\ emph {i.e} \ oneDot WFLW，300W和COFW，我们的方法以一个明显的余量优于最先进的方法。我们提出的方法在WFLW上达到了4.05 \％的平均误差，300W全设置的2.93 \％平均误差和COFW上的3.71 \％平均误差。
Deep learning technique has dramatically boosted the performance of face alignment algorithms. However, due to large variability and lack of samples, the alignment problem in unconstrained situations, \emph{e.g}\onedot large head poses, exaggerated expression, and uneven illumination, is still largely unsolved. In this paper, we explore the instincts and reasons behind our two proposals, \emph{i.e}\onedot Propagation Module and Focal Wing Loss, to tackle the problem. Concretely, we present a novel structure-infused face alignment algorithm based on heatmap regression via propagating landmark heatmaps to boundary heatmaps, which provide structure information for further attention map generation. Moreover, we propose a Focal Wing Loss for mining and emphasizing the difficult samples under in-the-wild condition. In addition, we adopt methods like CoordConv and Anti-aliased CNN from other fields that address the shift-variance problem of CNN for face alignment. When implementing extensive experiments on different benchmarks, \emph{i.e}\onedot WFLW, 300W, and COFW, our method outperforms state-of-the-arts by a significant margin. Our proposed approach achieves 4.05\% mean error on WFLW, 2.93\% mean error on 300W full-set, and 3.71\% mean error on COFW.
