论文标题
快速准确：面部对齐的结构相干组件
Fast and Accurate: Structure Coherence Component for Face Alignment
论文作者
论文摘要
在本文中，我们提出了一种快速准确的坐标回归方法，以进行面部比对。与通常采用完全连接的图层以将特征地图转换为地标协调的大多数现有面部标志性回归方法不同，我们提出了一个结构相干组件，以明确考虑面部标志之间的关系。由于人脸的几何结构，不同面部零件之间的结构连贯性为有效定位面部标记提供了重要的线索。但是，完全连接的层中的密集连接过度过度，使重要的提示无法与所有连接区分开。取而代之的是，我们的结构相干组件利用动态的稀疏图结构来传递最相关的地标之间的特征。此外，我们提出了一种新型的目标函数，称为软翼损失，以提高准确性。对包括WFLW，COFW和300W在内的三个流行基准测试的广泛实验证明了该方法的有效性，以快速的速度实现了最先进的性能。我们的方法对于挑战性案例尤其强大，导致COFW和WFLW数据集中的失败率（0％和2.88％）。
In this paper, we propose a fast and accurate coordinate regression method for face alignment. Unlike most existing facial landmark regression methods which usually employ fully connected layers to convert feature maps into landmark coordinate, we present a structure coherence component to explicitly take the relation among facial landmarks into account. Due to the geometric structure of human face, structure coherence between different facial parts provides important cues for effectively localizing facial landmarks. However, the dense connection in the fully connected layers overuses such coherence, making the important cues unable to be distinguished from all connections. Instead, our structure coherence component leverages a dynamic sparse graph structure to passing features among the most related landmarks. Furthermore, we propose a novel objective function, named Soft Wing loss, to improve the accuracy. Extensive experiments on three popular benchmarks, including WFLW, COFW and 300W, demonstrate the effectiveness of the proposed method, achieving state-of-the-art performance with fast speed. Our approach is especially robust to challenging cases resulting in impressively low failure rate (0% and 2.88%) in COFW and WFLW datasets.
